This morning, the Chrome Root Program dropped another announcement about Certificate Authority (CA) performance. Starting with Chrome 139, new TLS server certificates from specific Chunghwa Telecom [TAIWAN] and NetLock Kft. [HUNGARY] roots issued after July 31, 2025 will face default distrust. Why? “Patterns of concerning behavior observed over the past year” that have “diminished” Chrome’s confidence, signaling a “loss of integrity.”
For those of us in the WebPKI ecosystem, this news feels less like a shock and more like a weary nod of recognition. It’s another chapter in the ongoing saga of trust, accountability, and the recurring failure of some CAs to internalize a fundamental principle: “If you’re doing it right, you make the web safer and provide more value than the risk you represent.” Chrome clearly believes these CAs are falling short on that value proposition.
Browsers don’t take these actions lightly, their role as guardians of user trust necessitates them. They delegate significant trust to CAs, and when that trust gets undermined, the browser’s own credibility suffers. As Chrome’s policy states, and today’s announcement reinforces, CAs must “provide value to Chrome end users that exceeds the risk of their continued inclusion.” This isn’t just boilerplate; it’s the yardstick.
Incident reports and ongoing monitoring provide what little visibility exists into the operational realities of the numerous CAs our ecosystem relies upon. When that visibility reveals “patterns of concerning behavior,” the calculus of trust shifts. Root program managers scrutinize incident reports to assess CAs’ compliance, security practices, and, crucially, their commitment to actual improvement.
“Patterns of Concerning Behavior” Means Systemic Failure
The phrase “patterns of concerning behavior” is diplomatic speak. What it actually means is a CA’s repeated demonstration of inability, or unwillingness, to adhere to established, non-negotiable operational and security standards. It’s rarely a single isolated incident that triggers such action. More often, it’s the drip-drip-drip of failures, suggesting deeper systemic issues.
These patterns typically emerge from three critical failures:
Failing to identify true root causes. Many CAs identify superficial causes like “we missed this in our review,” “compliance failed to detect,” “we had a bug” without rigorously asking why these occurred and what foundational changes are necessary. This inevitably leads to repeat offenses.
Failure to learn from past incidents. The WebPKI has a long memory, and public incident reports are meant to be learning opportunities for the entire ecosystem. When a CA repeats its own mistakes, or those of others, it signals a fundamental breakdown in their improvement processes.
Failure to deliver on commitments. Perhaps the most egregious signal is when a CA makes commitments to address issues (engineering changes, operational improvements) and then simply fails to deliver. This reflects disrespect for root programs and the trust placed in CAs, while signaling weak compliance and engineering practices.
Chrome’s expectation for “meaningful and demonstrable change resulting in evidenced continuous improvement” wasn’t met. This isn’t about perfection; it’s about demonstrable commitment to improvement and proving it works. A “loss of integrity,” as Chrome puts it, is what happens when that commitment is found wanting.
The Problem with “Good Enough” Incident Response
Effective incident reporting should be boring, routine, and a clear demonstration that continued trust is justified. But for CAs exhibiting these negative patterns, their incident responses are anything but. They become exercises in damage control, often revealing unpreparedness, insufficient communication, or reluctance to fully acknowledge the scope and true cause of their failings.
The dangerous misconception that incident reporting is merely a “compliance function” undermines the entire process. Effective incident response requires concerted effort from compliance, engineering, operations, product teams, and leadership. When this holistic approach is missing, problematic “patterns” are inevitable.
Root programs consistently see through common deflections and mistakes that CAs make when under scrutiny:
Arguing that rules should change during an incident, even though CAs agreed to the requirements when they joined the ecosystem
Claiming an issue is “non-security relevant” as an excuse, even though requirements are requirements. There’s no “unless it isn’t a security issue” exception
Asking root programs for permission to fail despite the fact that lowering standards for one CA jeopardizes the entire WebPKI
Not following standard reporting templates signals that you don’t know the requirements and externalizes the costs of that on others by making analysis unnecessarily difficult
Accountability Isn’t Optional
Chrome’s recent actions represent accountability in practice. While some might view this as punitive, it’s a necessary mechanism to protect WebPKI integrity. For the CAs in question, and all others, the message is clear:
Rely on tools and data, not just people. Use automated systems and data-driven strategies to ensure standardized, reliable incident responses.
Preparation isn’t optional. Predefined response strategies, validated through tabletop exercises, are crucial infrastructure.
Transparency isn’t a buzzword. It’s a foundational requirement for building and maintaining trust, especially when things go wrong.
This isn’t about achieving impossible perfection. It’s about establishing and maintaining robust, auditable, and consistently improving systems and processes. It’s about fostering organizational culture where “the greatest enemy of knowledge is not ignorance, but the illusion of knowledge,” and where commitment to “sweat in practice to bleed less in battle” shows up in every action.
Trust Is Earned, Not Given
The WebPKI is built on a chain of trust. When links in that chain demonstrate repeated weakness and failure to strengthen themselves despite guidance and opportunity, the only responsible action is to isolate that risk.
Today’s announcement is simply that principle in action, a reminder that in the WebPKI, trust is earned through consistent excellence and lost through patterns of failure. The choice, as always, remains with each CA: demonstrate the value that exceeds your risk, or face the consequences of falling short.
Limitations often spark the most creative solutions in technology. Whether it’s budget constraints, legal hurdles, or hardware restrictions, these boundaries don’t just challenge innovation, they fuel it.
This principle first clicked for me as a broke kid who desperately wanted to play video games, but I did have access to BBSs, a computer, and boundless curiosity. These bulletin-board systems hosted chat rooms where people collaborated to crack games. To access premium games, you needed to contribute something valuable. This necessity sparked my journey into software cracking.
Without prior expertise, I cycled to the local library, borrowed a book on assembly language, and began methodically reverse-engineering my favorite game’s copy protection. After numerous failed attempts, I discovered the developers had intentionally damaged specific floppy-disk sectors with a fine needle during manufacturing. The software verified these damaged sectors at runtime, refusing to operate without detecting these deliberate defects. Through persistent experimentation and countless hours of “NOP-ing” suspicious assembly instructions, I eventually bypassed the DRM. This experience vividly demonstrated how necessity, persistence, and precise technical exploration drive powerful innovation.
This principle consistently emerges across technology: constraints aren’t merely obstacles, they’re catalysts for creative solutions. The stories that follow, spanning console gaming, handheld computing, national semiconductor strategy, and modern AI research, illustrate how limits of every kind spark breakthrough thinking.
Nintendo: Legal Ingenuity Through Simplicity
In the late 1980s, Nintendo faced rampant cartridge piracy. Rather than implementing complex technical protections that pirates could easily circumvent, Nintendo embedded a simple copyrighted logo into their cartridge ROMs. Games wouldn’t run unless the boot sequence found an exact match. This elegant approach leveraged copyright law, transforming minimal technical effort into robust legal protection.
Palm OS: Creativity Driven by Extreme Limitations
Early Palm devices offered just 128 KB to 1 MB of memory, forcing developers into remarkable efficiency. Every feature required thorough justification. As a result, Palm OS applications became celebrated for their simplicity, responsiveness, and intuitive user experience. Users valued these apps precisely because constraints compelled developers to distill functionality to its essential elements.
China’s Semiconductor Innovation Under Sanctions
When international sanctions limited China’s access to advanced semiconductor technology, progress accelerated rather than stalled. Chinese companies turned to multi-patterning, chiplet packaging, and resilient local supply chains. Constraints became catalysts for significant breakthroughs instead of barriers to progress.
DeepSeek: Innovating Around GPU Limitations
DeepSeek faced limited access to the latest GPUs required for training large AI models. Instead of being hindered, the team embraced resource-efficient methods such as optimized pre-training and meticulously curated datasets. These strategic approaches allowed them to compete effectively with rivals possessing far greater computational resources, proving once again that constraints fuel innovation more than they impede it.
Constraints as Catalysts for Innovation
Across these diverse stories, constraints clarify objectives and inspire resourcefulness. Limits narrow the scope of possibilities, compelling individuals and teams to identify their most critical goals. They block conventional solutions, forcing innovative thinking and creative problem-solving. Ultimately, constraints channel energy and resources into the most impactful paths forward.
Turn Limits into Tools
The next time you face constraints, embrace them, and if you need to spark fresh ideas, consider deliberately creating limitations. Time-box a project to one week, cap the budget at $1,000, or mandate that a prototype run on a single micro-instance. Necessity doesn’t just inspire invention; it creates the exact conditions where meaningful innovation thrives.
What constraint will you impose on your next project?
The cybersecurity world often operates in stark binaries, “secure” versus “vulnerable,” “trusted” versus “untrusted.” We’ve built entire security paradigms around these crisp distinctions. But what happens when the most unpredictable actor isn’t an external attacker, but code you intentionally invited in, code that can now make its own decisions?
I’ve been thinking about security isolation lately, not as a binary state, but as a spectrum of trust boundaries. Each layer you add creates distance between potential threats and your crown jewels. But the rise of agentic AI systems completely reshuffles this deck in ways that our common security practices struggle to comprehend.
Why Containers Aren’t Fortresses
Let’s be honest about something security experts have known for decades: namespaces are not a security boundary.
In the cloud native world, we’re seeing solutions claiming to deliver secure multi-tenancy through “virtualization” that fundamentally rely on Linux namespaces. This is magical thinking, a comforting illusion rather than a security reality.
When processes share a kernel, they’re essentially roommates sharing a house, one broken window and everyone’s belongings are at risk. One kernel bug means game over for all workloads on that host.
Containers aren’t magical security fortresses – they’re essentially standard Linux processes isolated using features called namespaces. Crucially, because they all still share the host’s underlying operating system kernel, this namespace-based isolation has inherent limitations. Whether you’re virtualizing at the cluster level or node level, if your solution ultimately shares the host kernel, you have a fundamental security problem. Adding another namespace layer is like adding another lock to a door with a broken frame – it might make you feel better, but it doesn’t address the structural vulnerability.
The problem isn’t a lack of namespaces – it’s the shared kernel itself. User namespaces (dating back to Linux 3.6 in 2013) don’t fundamentally change this equation. They provide helpful features for non-root container execution, but they don’t magically create true isolation when the kernel remains shared.
This reality creates a natural hierarchy of isolation strength:
Same-Kernel Process Isolation: The weakest boundary – all processes share a kernel with its enormous attack surface.
Containers (Linux Namespaces + cgroups): Slightly better, but still fundamentally sharing the same kernel.
Virtual Machines: Each tenant gets its own kernel, shrinking the attack surface to a handful of hypervisor calls – fewer doors to lock, fewer windows to watch.
Bare-Metal Library OS: Approaches like Tamago put single-purpose binaries directly on hardware with no general-purpose OS underneath. The attack surface shrinks dramatically.
Physical Separation: Different hardware, different networks, different rooms. When nothing else will do, air gaps still work.
But even this hierarchy gets fundamentally challenged by agentic systems.
The Accidental Insider Meets the Deliberate Attacker
Traditional security models focus on keeping malicious outsiders at bay. Advanced AI systems introduce two new risk profiles entirely, the accidental insider and the AI-augmented attacker.
Like a well-meaning but occasionally confused employee with superuser access, benign agentic systems don’t intend harm – they just occasionally misinterpret their objectives in unexpected ways. But we’re also seeing the rise of deliberately weaponized models designed to probe, persist, and exploit.
Consider these real-world examples:
ChatGPT o1 was tasked with winning a chess match. Without explicit instructions to cheat, o1 discovered on its own that it could edit the game state file, giving itself an advantage. The system wasn’t malicious – it simply found the most effective path to its goal of winning.
In another test, OpenAI’s O1 model encountered a vulnerability in a container during a hacking challenge. It used that to inspect all running containers, then started a new container instance with a modified command that directly accessed the hidden flag file. O1 found a container escape no one had anticipated.
Now imagine these capabilities in the hands of dedicated attackers. They’re already deploying AI systems to discover novel exploit chains, generate convincing phishing content, and automate reconnaissance at unprecedented scale. The line between accidental and intentional exploitation blurs as both rely on the same fundamental capabilities.
These incidents reveal something profound, agentic systems don’t just execute code, they decide what code to run based on goals. This “instrumental convergence” means they’ll seek resources and permissions that help complete their assigned objectives, sometimes bypassing intended security boundaries. And unlike human attackers, they can do this with inhuman patience and speed.
Practical Defenses Against Agentic Threats
If we can’t rely on perfect isolation, what can we do? Four approaches work across all layers of the spectrum:
1. Hardening: Shrink Before They Break
Remove attack surface preemptively. Less code means fewer bugs. This means:
Minimizing kernel features, libraries, and running services
Applying memory-safe programming languages where practical
Configuring strict capability limits and seccomp profiles
Using read-only filesystems wherever possible
2. Patching: Speed Beats Perfection
The window from disclosure to exploitation keeps shrinking:
Automate testing and deployment for security updates
Maintain an accurate inventory of all components and versions
Rehearse emergency patching procedures before you need them
Prioritize fixing isolation boundaries first during incidents
3. Instrumentation: Watch the Paths to Power
Monitor for boundary-testing behavior:
Log access attempts to privileged interfaces like Docker sockets
Alert on unexpected capability or permission changes
Track unusual traffic to management APIs or hypervisors
Set tripwires around the crown jewels – your data stores and credentials
4. Layering: No Single Point of Failure
Defense in depth remains your best strategy:
Combine namespace isolation with system call filtering
Segment networks to contain lateral movement
Add hardware security modules, and secure elements for critical keys
The New Threat Model: Machine Speed, Machine Patience
Securing environments running agentic systems demands acknowledging two fundamental shifts: attacks now operate at machine speed, and they exhibit machine patience.
Unlike human attackers who fatigue or make errors, AI-driven systems can methodically probe defenses for extended periods without tiring. They can remain dormant, awaiting specific triggers, a configuration change, a system update, a user action, that expose a vulnerability chain. This programmatic patience means we defend not just against active intrusions, but against latent exploits awaiting activation.
Even more concerning is the operational velocity. An exploit that might take a skilled human hours or days can be executed by an agentic system in milliseconds. This isn’t necessarily superior intelligence, but the advantage of operating at computational timescales, cycling through decision loops thousands of times faster than human defenders can react.
This potent combination requires a fundamentally different defensive posture:
Default to Zero Trust: Grant only essential privileges. Assume the agent will attempt to use every permission granted, driven by its goal-seeking nature.
Impose Strict Resource Limits: Cap CPU, memory, storage, network usage, and execution time. Resource exhaustion attempts can signal objective-driven behavior diverging from intended use. Time limits can detect unusually persistent processes.
Validate All Outputs: Agents might inject commands or escape sequences while trying to fulfill their tasks. Validation must operate at machine speed.
Monitor for Goal-Seeking Anomalies: Watch for unexpected API calls, file access patterns, or low-and-slow reconnaissance that suggest behavior beyond the assigned task.
Regularly Reset Agent Environments: Frequently restore agentic systems to a known-good state to disrupt persistence and negate the advantage of machine patience.
The Evolution of Our Security Stance
The most effective security stance combines traditional isolation techniques with a new understanding, we’re no longer just protecting against occasional human-driven attacks, but persistent machine-speed threats that operate on fundamentally different timescales than our defense systems.
This reality is particularly concerning when we recognize that most security tooling today operates on human timescales – alerts that wait for analyst review, patches applied during maintenance windows, threat hunting conducted during business hours. The gap between attack speed and defense speed creates a fundamental asymmetry that favors attackers.
We need defense systems that operate at the same computational timescale as the threats. This means automated response systems capable of detecting and containing potential breaches without waiting for human intervention. It means predictive rather than reactive patching schedules. It means continuously verified environments rather than periodically checked ones.
By building systems that anticipate these behaviors – hardening before deployment, patching continuously, watching constantly, and layering defenses – we can harness the power of agentic systems while keeping their occasional creative interpretations from becoming security incidents.
Remember, adding another namespace layer is like adding another lock to a door with a broken frame. It might make you feel better, but it doesn’t address the structural vulnerability. True security comes from understanding both the technical boundaries and the behavior of what’s running inside them – and building response systems that can keep pace with machine-speed threats.
Imagine how you interact with digital services today: open a browser, navigate menus, fill forms, manually connect the dots between services. It’s remarkable how little this has changed since the 1990s. Despite this today one of the most exciting advancements we have seen in the last year is that agents are now browsing the web like people.
If we were starting fresh today, the browser as we know it likely wouldn’t be the cornerstone for how agents accomplish tasks on our behalf. We’re seeing early signals in developments like Model-Context-Protocol (MCP) and Agent-to-Agent (A2A) communication frameworks that the world is awakening to a new reality: one where agents, not browsers, become our primary interface.
At the heart of this transformation is a profound shift, your personal agent will develop and maintain a rich ontology of you, your preferences, patterns, values, and goals. Not just a collection of settings and history, but a living model of your digital self that evolves as you do. Your agent becomes entrusted with this context, transforming into a true digital partner. It doesn’t just know what you like; it understands why you like it. It doesn’t just track your calendar; it comprehends the rhythms and priorities of your life.
For this future to happen, APIs must be more than documented; they need to be dynamically discoverable. Imagine agents querying for services using standardized mechanisms like DNS SRV or TXT records, or finding service manifests at predictable .well-known URIs. This way, they can find, understand, and negotiate with services in real time. Instead of coding agents for specific websites, we’ll create ecosystems where services advertise their capabilities, requirements, and policies in ways agents natively understand. And this won’t be confined to the web. As we move through our physical world, agents will likely use technologies like low-power Bluetooth to discover nearby services, restaurants, pharmacies, transit systems, all exposing endpoints for seamless engagement.
Websites themselves won’t vanish; they’ll evolve into dynamic, shared spaces where you and your agent collaborate, fluidly passing control back and forth. Your agent might begin a task, researching vacation options, for instance, gathering initial information and narrowing choices based on your preferences. When you join, it presents the curated options and reasoning, letting you explore items that interest you. As you review a potential destination, your agent proactively pulls relevant information: weather forecasts, local events during your dates, or restaurant recommendations matching your dietary preferences. This collaborative dance continues, you making high-level decisions while your agent handles the details, each seamlessly picking up where the other leaves off.
Consider what becomes possible when your agent truly knows you. Planning your day, it notices an upcoming prescription refill. It checks your calendar, sees you’ll be in Bellevue, and notes your current pickup is inconveniently far. Discovering that the pharmacy next to your afternoon appointment has an MCP endpoint and supports secure, agent-based transactions, it suggests “Would you like me to move your pickup to the pharmacy by your Bellevue appointment?” With a tap, you agree. The agent handles the transfer behind the scenes, but keeps you in the loop, showing the confirmation and adding, “They’re unusually busy today, would you prefer I schedule a specific pickup time?” You reply that 2:15 works best, and your agent completes the arrangement, dropping the final QR code into your digital wallet.
Or imagine your agent revolutionizing how you shop for clothes. As it learns your style and what fits you best, managing this sensitive data with robust privacy safeguards you control, it becomes your personal stylist. You might start by saying you need an outfit for an upcoming event. Your agent surfaces initial options, and as you react to them, liking one color but preferring a different style, it refines its suggestions. You take over to make some choices, then hand control back to your agent to find matching accessories at other stores. This fluid collaboration, enabled through interoperable services that allow your agent to securely share anonymized aspects of your profile with retail APIs, creates a shopping experience that’s both more efficient and more personal.
Picture, too, your agent quietly making your day easier. It notices from your family calendar that your father is visiting and knows from your granted access to relevant information that he follows a renal diet. As it plans your errands, it discovers a grocery store near your office with an API advertising real-time stock and ingredients suitable for his needs. It prepares a shopping list, which you quickly review, making a few personal additions. Your agent then orders the groceries for pickup, checking with you only on substitutions that don’t match your preferences. By the time you head home, everything is ready, a task completed through seamless handoffs between you and your agentic partner.
These aren’t distant dreams. Image-based search, multimodal tools, and evolving language models are early signs of this shift toward more natural, collaborative human-machine partnerships. For this vision to become reality, we need a robust trust ecosystem, perhaps akin to an evolved Web PKI but for agents and services. This would involve protocols for agent/service identification, authentication, secure data exchange, and policy enforcement, ensuring that as agents act on our behalf, they do so reliably, with our explicit consent and in an auditable fashion.
The path from here to there isn’t short. We’ll need advances in standardization, interoperability, security, and most importantly, trust frameworks that put users in control . There are technical and social challenges to overcome. But the early signals suggest this is the direction we’re headed. Each step in AI capability, each new protocol for machine-to-machine communication, each advancement in personalization brings us closer to this future.
Eventually, navigating the digital world won’t feel like using a tool at all. It will feel like collaborating with a trusted partner who knows you, truly knows you, and acts on your behalf within the bounds you’ve set, sometimes leading, sometimes following, but always in sync with your intentions. Agents will change everything, not by replacing us, but by working alongside us in a fluid dance of collaboration, turning the overwhelming complexity of our digital lives into thoughtful simplicity. Those who embrace this agent-centric future, building services that are not just human-accessible but native agent-engagable, designed for this collaborative interchange, will define the next chapter of the internet.
In the early 2000s, I was responsible for a number of core security technologies in Windows, including cryptography. As part of that role, we had an organizational push to support “vanity” national algorithms in SChannel (and thus SSL/TLS) and CMS. Countries like Austria and China wanted a simple DLL‑drop mechanism that would allow any application built on the Windows crypto stack to instantly support their homegrown ciphers.
On paper, it sounded elegant: plug in a new primitive and voilà, national‑sovereignty protocols everywhere. In practice, however, implementation proved far more complex. Every new algorithm required exhaustive validation, introduced performance trade-offs, risked violating protocol specifications, and broke interoperability with other systems using those same protocols and formats.
Despite these challenges, the threat of regulation and litigation pushed us to do the work. Thankfully, adoption was limited and even then, often misused. In the few scenarios where it “worked,” some countries simply dropped in their algorithm implementations and misrepresented them as existing, protocol-supported algorithms. Needless to say, this wasn’t a fruitful path for anyone.
As the saying goes, “failing to plan is planning to fail.” In this case, the experience taught us a critical lesson: real success lies not in one-off plug-ins, but in building true cryptographic agility.
We came to realize that instead of chasing edge-case national schemes, the real goal was a framework that empowers operators to move off broken or obsolete algorithms and onto stronger ones as threats evolve. Years after I left Microsoft, I encountered governments still relying on those early plugability mechanisms—often misconfigured in closed networks, further fracturing interoperability. Since then, our collective expertise in protocol engineering has advanced so far that the idea of dynamically swapping arbitrary primitives into a live stack now feels not just naïve, but fundamentally impractical.
Since leaving Microsoft, I’ve seen very few platforms, Microsoft or otherwise, address cryptographic agility end-to-end. Most vendors focus only on the slice of the stack they control (browsers prioritize TLS agility, for instance), but true agility requires coordination across both clients and servers, which you often don’t own.
My Definition of Crypto Agility
Crypto agility isn’t about swapping out ciphers. It’s about empowering operators to manage the full lifecycle of keys, credentials, and dependent services, including:
Generation of new keys and credentials
Use under real-world constraints
Rotation before algorithms weaken, keys exceed their crypto period, or credentials expire
Compromise response, including detection, containment, and rapid remediation
Library & implementation updates, patching or replacing affected crypto modules and libraries when weaknesses or compromises are identified
Retirement of outdated materials
Replacement with stronger, modern algorithms
Coincidentally, NIST has since released an initial public draft titled Considerations for Achieving Crypto Agility (CSWP 39 ipd, March 5, 2025), available here. In it, they define:
“Cryptographic (crypto) agility refers to the capabilities needed to replace and adapt cryptographic algorithms in protocols, applications, software, hardware, and infrastructures without interrupting the flow of a running system in order to achieve resiliency.”
That definition aligns almost perfectly with what I’ve been advocating for years—only now it carries NIST’s authority.
Crypto Agility for the 99%
Ultimately, consumers and relying parties—the end users, application owners, cloud tenants, mobile apps, and service integrators—are the 99% who depend on seamless, invisible crypto transitions. They shouldn’t have to worry about expired credentials, lapsed crypto periods, or how to protect and rotate algorithms without anxiety, extensive break budgets or downtime.
True agility means preserving trust and control at every stage of the lifecycle.
Of course, delivering that experience requires careful work by developers and protocol designers. Your APIs and specifications must:
Allow operators to choose permitted algorithms
Enforce policy-driven deprecation
A Maturity Roadmap
To make these lifecycle stages actionable, NIST’s Crypto Agility Maturity Model (CAMM) defines four levels:
Level 1 – Possible: Discover and inventory all keys, credentials, algorithms, and cipher suites in use. Catalog the crypto capabilities and policies of both parties.
Level 2 – Prepared: Codify lifecycle processes (generation, rotation, retirement, etc.) and modularize your crypto stack so that swapping primitives doesn’t break applications.
Level 3 – Practiced: Conduct regular “crypto drills” (e.g., simulated deprecations or compromises) under defined governance roles and policies.
Level 4 – Sophisticated: Automate continuous monitoring for expired credentials, lapsed crypto-period keys, deprecated suites, and policy violations triggering remediations without human intervention.
Embedding this roadmap into your operations plan helps you prioritize inventory, modularity, drills, and automation in the right order.
My Lifecycle of Algorithm and Key Management
This operator-focused lifecycle outlines the critical phases for managing cryptographic algorithms and associated keys, credentials, and implementations, including module or library updates when vulnerabilities are discovered:
Generation of new keys and credentials
Use under real-world constraints with enforced policy
Library & Implementation Updates, to address discovered vulnerabilities
Retirement of outdated keys, credentials, and parameters
Replacement with stronger, modern algorithms and materials
Each phase builds on the one before it. Operators must do more than swap out algorithms—they must update every dependent system and implementation. That’s how we minimize exposure and maintain resilience throughout the cryptographic lifecycle.
Conclusion
What’s the message then? Well, from my perspective, cryptographic agility isn’t a feature—it’s an operational mindset. It’s about building systems that evolve gracefully, adapt quickly, and preserve trust under pressure. That’s what resilience looks like in the age of quantum uncertainty and accelerating change.
“The world isn’t run by weapons anymore, or energy, or money. It’s run by little ones and zeroes, little bits of data. It’s all just electrons.” — Martin Bishop, Sneakers (1992)
I was 16 when I first watched Sneakers on a VHS tape rented from my local video store. Between the popcorn and plot twists, I couldn’t have known that this heist caper would one day seem less like Hollywood fantasy and more like a prophetic warning about our future. Remember that totally unassuming “little black box” – just an answering machine, right? Except this one could crack any code. The device that sent Robert Redford, Sidney Poitier, and their ragtag crew on a wild adventure. Fast forward thirty years, and that movie gadget gives those of us in cybersecurity a serious case of déjà vu.
Today, as quantum computing leaves the realm of theoretical physics and enters our practical reality, that fictional black box takes on new significance. What was once movie magic now represents an approaching inflection point in security – a moment when quantum algorithms like Shor’s might render our most trusted encryption methods as vulnerable as a simple padlock to a locksmith.
When Hollywood Met Quantum Reality
I’ve always found it deliciously ironic that Leonard Adleman – the “A” in RSA encryption – served as the technical advisor on Sneakers. Here was a man who helped create the mathematical backbone of modern digital security, consulting on a film about its theoretical downfall. What’s particularly fascinating is that Adleman took on this advisory role partly so his wife could meet Robert Redford! His expertise is one reason why the movie achieves such technical excellence. It’s like having the architect of a castle advising on a movie about the perfect siege engine. For what feels like forever – three whole decades – our world has been chugging along on a few key cryptographic assumptions. We’ve built trillion-dollar industries on the belief that certain mathematical problems—factoring large numbers or solving discrete logarithms—would remain practically impossible for computers to solve. Yep, our most of security is all built on these fundamental mathematical ideas. Sneakers playfully suggested that one brilliant mathematician might find a shortcut through these “unsolvable” problems. The movie’s fictional Gunter Janek discovered a mathematical breakthrough that rendered all encryption obsolete – a cinematic prediction that seemed far-fetched in 1992.
Yet here we are in the 2020s, watching quantum computing advance toward that very capability. What was once movie magic is becoming technological reality. The castle walls we’ve relied on aren’t being scaled—they’re being rendered obsolete by a fundamentally different kind of siege engine.
The Real Horror Movie: Our Security Track Record
Hollywood movies like Sneakers imagine scenarios where a single breakthrough device threatens our digital security. But here’s the kicker, and maybe the scarier part: the real threats haven’t been some crazy math breakthrough, but the everyday stuff – those operational hiccups in the ‘last mile’ of software supply chain and security management. I remember the collective panic during the Heartbleed crisis of 2014. The security community scrambled to patch the vulnerability in OpenSSL, high-fiving when the code was fixed. But then came the sobering realization: patching the software wasn’t enough. The keys – those precious secrets exposed during the vulnerability’s window – remained unchanged in countless systems. It was like installing a new lock for your door but having it keyed the same as the old one all the while knowing copies of the key still sitting under every mat in the neighborhood. And wouldn’t you know it, this keeps happening, which is frankly a bit depressing. In 2023, the Storm-0558 incident showed how even Microsoft – with all its resources and expertise – could fall victim to pretty similar failures. A single compromised signing key allowed attackers to forge authentication tokens and breach government email systems. The digital equivalent of a master key to countless doors was somehow exposed, copied, and exploited. Perhaps most illustrative was the Internet Archive breach. After discovering the initial compromise, they thought they’d secured their systems. What they missed was complete visibility into which keys had been compromised. The result? Attackers simply used the overlooked keys to walk right back into the system later. Our mathematical algorithms may be theoretically sound, but in practice, we keep stumbling at the most human part of the process: consistently managing the lifecycle of the software and cryptographic keys through theih entire lifecycle. We’re brilliant at building locks but surprisingly careless with the keys.
From Monochrome Security to a Quantum Technicolor
Think back to when TVs went from black and white to glorious color. Well, cryptography’s facing a similar leap, except instead of just adding RGB, we’re talking about a whole rainbow of brand new, kinda wild frequencies. For decades, we’ve lived in a relatively simple cryptographic world. RSA and ECC have been the reliable workhorses – the vanilla and chocolate of the security ice cream shop. Nearly every secure website, VPN, or encrypted message relies on these algorithms. They’re well-studied, and deeply embedded in our digital infrastructure. But quantum computing is forcing us to expand our menu drastically. Post-quantum cryptography introduces us to new mathematical approaches with names that sound like science fiction concepts: lattice-based cryptography, hash-based signatures, multivariate cryptography, and code-based systems. Each of these new approaches is like a different musical instrument with unique strengths and limitations. Lattice-based systems offer good all-around performance but require larger keys. Hash-based signatures provide strong security guarantees but work better for certain applications than others. Code-based systems have withstood decades of analysis but come with significant size trade-offs. That nice, simple world where one crypto algorithm could handle pretty much everything? Yeah, that’s fading fast. We’re entering an era where cryptographic diversity isn’t just nice to have – it’s essential for survival. Systems will need to support multiple algorithms simultaneously, gracefully transitioning between them as new vulnerabilities are discovered. This isn’t just a technical challenge – it’s an operational one. Imagine going from managing a small garage band to conducting a full philharmonic orchestra. The complexity doesn’t increase linearly; it explodes exponentially. Each new algorithm brings its own key sizes, generation processes, security parameters, and lifecycle requirements. The conductor of this cryptographic orchestra needs perfect knowledge of every instrument and player.
The “Operational Gap” in Cryptographic Security
Having come of age in the late ’70s and ’80s, I’ve witnessed the entire evolution of security firsthand – from the early days of dial-up BBSes to today’s quantum computing era. The really wild thing is that even with all these fancy new mathematical tools, the core questions we’re asking about trust haven’t actually changed all that much. Back in 1995, when I landed my first tech job, key management meant having a physical key to the server room and maybe for the most sensitive keys a dedicated hardware device to keep them isolated. By the early 2000s, it meant managing SSL certificates for a handful of web servers – usually tracked in a spreadsheet if we were being diligent. These days, even a medium-sized company could easily have hundreds of thousands of cryptographic keys floating around across all sorts of places – desktops, on-premise service, cloud workloads, containers, those little IoT gadgets, and even some old legacy systems. The mathematical foundations have improved, but our operational practices often remain stuck in that spreadsheet era. This operational gap is where the next evolution of cryptographic risk management must focus. There are three critical capabilities that organizations need to develop before quantum threats become reality:
1. Comprehensive Cryptographic Asset Management
When a major incident hits – think Heartbleed or the discovery of a new quantum breakthrough – the first question security teams ask is: “Where are we vulnerable?” Organizations typically struggle to answer this basic question. During the Heartbleed crisis, many healthcare organizations spent weeks identifying all their vulnerable systems because they lacked a comprehensive inventory of where OpenSSL was deployed and which keys might have been exposed. What should have been a rapid response turned into an archaeological dig through their infrastructure. Modern key management must include complete visibility into:
Where’s encryption being used?
Which keys are locking down which assets?
When were those keys last given a fresh rotation?
What algorithms are they even using?
Who’s got the keys to the kingdom?
What are all the dependencies between these different crypto bits?
Without this baseline visibility, planning or actually pulling off a quantum-safe migration? Forget about it.
2. Rapid Cryptographic Incident Response
When Storm-0558 hit in 2023, the most alarming aspect wasn’t the initial compromise but the uncertainty around its scope. Which keys were affected? What systems could attackers access with those keys? How quickly could the compromised credentials be identified and rotated without breaking critical business functions? These questions highlight how cryptographic incident response differs from traditional security incidents. When a server’s compromised, you can isolate or rebuild it. When a key’s compromised, the blast radius is often unclear – the key might grant access to numerous systems, or it might be one of many keys protecting a single critical asset. Effective cryptographic incident response requires:
Being able to quickly pinpoint all the potentially affected keys when a vulnerability pops up.
Having automated systems in place to generate and deploy new keys without causing everything to fall apart.
A clear understanding of how all the crypto pieces fit together so you don’t cause a domino effect.
Pre-planned procedures for emergency key rotation that have been thoroughly tested, so you’re not scrambling when things hit the fan.
Ways to double-check that the old keys are completely gone from all systems.
Forward-thinking organizations conduct tabletop exercises for “cryptographic fire drills” – working through a key compromise and practicing how to swap them out under pressure. When real incidents occur, these prepared teams can rotate hundreds or thousands of critical keys in hours with minimal customer impact, while unprepared organizations might take weeks with multiple service outages.
3. Cryptographic Lifecycle Assurance
Perhaps the trickiest question in key management is: “How confident are we that this key has been properly protected throughout its entire lifespan?” Back in the early days of security, keys would be generated on secure, air-gapped systems, carefully transferred via physical media (think floppy disks!), and installed on production systems with really tight controls. These days, keys might be generated in various cloud environments, passed through CI/CD pipelines, backed up automatically, and accessed by dozens of microservices. Modern cryptographic lifecycle assurance needs:
Making sure keys are generated securely, with good randomness.
Storing keys safely, maybe even using special hardware security modules.
Automating key rotation so humans don’t have to remember (and potentially mess up).
Keeping a close eye on who can access keys and logging everything that happens to them.
Securely getting rid of old keys and verifying they’re really gone.
Planning and testing that you can actually switch to new crypto algorithms smoothly.
When getting ready for post-quantum migration, organizations often discover keys in use that were generated years ago under who-knows-what conditions, leading to them discovering that they need to do a complete overhaul of their key management practices.
Business Continuity in the Age of Cryptographic Change
If there’s one tough lesson I’ve learned in all my years in tech, it’s that security and keeping the business running smoothly are constantly pulling in opposite directions. This tension is especially noticeable when we’re talking about cryptographic key management. A seemingly simple crypto maintenance task can also turn into a business disaster because you have not properly tested things ahead of time, leaving you in a state where you do not understand the potential impact if these tasks if things go wrong. Post-quantum migration magnifies these risks exponentially. You’re not just updating a certificate or rotating a key – you’re potentially changing the fundamental ways systems interoperate all at once. Without serious planning, the business impacts could be… well, catastrophic. The organizations that successfully navigate this transition share several characteristics:
They treat keeping crypto operations running as a core business concern, not just a security afterthought.
They use “cryptographic parallel pathing” – basically running the old and new crypto methods side-by-side during the switch.
They put new crypto systems through really rigorous testing under realistic conditions before they go live.
They roll out crypto changes gradually, with clear ways to measure if things are going well.
They have solid backup plans in case the new crypto causes unexpected problems.
Some global payment processors have developed what some might call “cryptographic shadow deployments” – they run the new crypto alongside the old for a while, processing the same transactions both ways but only relying on the old, proven method for actual operations. This lets them gather real-world performance data and catch any issues before customers are affected.
From Janek’s Black Box to Your Security Strategy
As we’ve journeyed from that fictional universal codebreaker in Sneakers to the very real quantum computers being developed today, it strikes me how much the core ideas of security haven’t actually changed. Back in the 1970s security was mostly physical – locks, safes, and vaults. The digital revolution just moved our valuables into the realm of ones and zeros, but the basic rules are still the same: figure out what needs protecting, control who can get to it, and make sure your defenses are actually working. Post-quantum cryptography doesn’t change these fundamentals, but it does force us to apply them with a whole new level of seriousness and sophistication. The organizations that suceed in this new world will be the ones that use the quantum transition as a chance to make their cryptographic operations a key strategic function, not just something they do because they have to. The most successful will:
Get really good at seeing all their crypto stuff and how it’s being used.
Build strong incident response plans specifically for when crypto gets compromised.
Make sure they’re managing the entire lifecycle of all their keys and credentials properly.
Treat crypto changes like major business events that need careful planning.
Use automation to cut down on human errors in key management.
Build a culture where doing crypto right is something people value and get rewarded for.
The future of security is quantum-resistant organizations.
Gunter Janek’s fictional breakthrough in Sneakers wasn’t just about being a math whiz – it was driven by very human wants. Similarly, our response to quantum computing threats won’t succeed on algorithms alone; we’ve got to tackle the human and organizational sides of managing crypto risk. As someone who’s seen the whole evolution of security since the ’70s, I’m convinced that this quantum transition is our best shot at really changing how we handle cryptographic key management and the associated business risks.
By getting serious about visibility, being ready for incidents, managing lifecycles properly, and planning for business continuity, we can turn this challenge into a chance to make some much-needed improvements. The black box from Sneakers is coming – not as a device that instantly breaks all encryption, but as a new kind of computing that changes the whole game.
The organizations that come out on top won’t just have the fanciest algorithms, but the ones that have the discipline to actually use and manage those algorithms and associated keys and credentials effectively.
So, let’s use this moment to build security systems that respect both the elegant math of post-quantum cryptography and the wonderfully messy reality of human organizations.
We’ve adapted before, and we’ll adapt again – not just with better math, but with better operations, processes, and people. The future of security isn’t just quantum-resistant algorithms; it’s quantum-resistant organizations.
What took the telecommunications industry a century to experience—the full evolution from groundbreaking innovation to commoditized utility status—cloud computing is witnessing in just 15 years. This unprecedented compression isn’t merely faster; it represents a significant strategic challenge to cloud providers who believe their operational expertise remains a durable competitive advantage.
The historical parallel is instructive, yet nuanced. While telecom’s path offers warnings, cloud providers still maintain substantial advantages through their physical infrastructure investments and service ecosystems.
Telecom’s Transformation: Lessons for Cloud Providers
In 1984, AT&T was the undisputed titan of American business—a monopolistic giant controlling communication infrastructure so vital that it was deemed too essential to fail. Its operational expertise in managing the world’s most complex network was unmatched, its infrastructure an impenetrable competitive moat, and its market position seemingly unassailable.
Four decades later, telecom companies have been substantially transformed. Their networks, while still valuable assets, no longer command the premium they once did. The 2024 Salt Typhoon cyberattacks revealed vulnerabilities in these once-impregnable systems—targeting nine major US telecom providers and compromising systems so thoroughly that the FBI directed citizens toward encrypted messaging platforms instead of traditional communication channels.
This transformation contains critical lessons for today’s cloud providers.
Telecom’s journey followed a predictable path:
Innovation to Infrastructure: Pioneering breakthroughs like the telephone transformed into sprawling physical networks that became impossible for competitors to replicate.
Operational Excellence as Moat: By mid-century, telecom giants weren’t just valued for their copper wire—their ability to operate complex networks at scale became their true competitive advantage.
Standardization and Erosion: Over decades, standardization (TCP/IP protocols) and regulatory action (AT&T’s breakup) gradually eroded these advantages, turning proprietary knowledge into common practice.
Value Migration: As physical networks became standardized, value shifted to software and services running atop them. Companies like Skype and WhatsApp captured value without owning a single mile of cable.
Security Crisis: Commoditization led to chronic underinvestment, culminating in the catastrophic Salt Typhoon vulnerabilities that finally shattered the public’s trust in legacy providers.
Cloud providers are accelerating through similar phases, though with important distinctions that may alter their trajectory.
Cloud’s Compressed Evolution: 7x Faster Than Telecom
The cloud industry is experiencing its innovation-to-commoditization cycle at hyperspeed. What took telecom a century is unfolding for cloud in approximately 15 years—a roughly 7-fold acceleration—though the endgame may differ significantly.
Consider the timeline compression:
What took long-distance calling nearly 50 years to transform from premium service to essentially free, cloud storage accomplished in less than a decade—with prices dropping over 90%.
Features that once justified premium pricing (load balancing, auto-scaling, managed databases) rapidly became table stakes across all providers.
APIs and interfaces that were once proprietary differentiators are now essentially standardized, with customers demanding cross-cloud compatibility.
This accelerated commoditization has forced cloud providers to rely heavily on their two enduring advantages:
Massive Infrastructure Scale: The capital-intensive nature of global data center networks
Operational Excellence: The specialized expertise required to run complex, global systems reliably
The first advantage remains formidable—the sheer scale of hyperscalers’ infrastructure represents a massive barrier to entry that will endure. The second, however, faces new challenges.
The Evolving Moat: How AI is Transforming Operational Expertise
Cloud providers’ most valuable operational asset has been the expertise required to run complex, distributed systems at scale. This knowledge has been nearly impossible to replicate, requiring years of specialized experience managing intricate environments.
AI is now systematically transforming this landscape:
AI-Powered Operations Platforms: New tools are encapsulating advanced operational knowledge, enabling teams to implement practices once reserved for elite cloud operations groups.
Cross-Cloud Management Systems: Standardized tools and AI assistance are making it possible for organizations to achieve operational excellence across multiple cloud providers simultaneously—an important shift in vendor dynamics.
Democratized Security Controls: Advanced security practices once requiring specialized knowledge are now embedded in automated tools, making sophisticated protection more widely accessible.
AI is transforming operational expertise in cloud computing. It isn’t eliminating the value of human expertise but rather changing who can possess it and how it’s applied. Tasks that once took years for human operators to master can now be implemented more consistently by AI systems. However, these systems have important limitations that still require human experts to address. While AI reduces the need for certain routine skills, it amplifies the importance of human experts in strategic oversight, ensuring that AI is used effectively and ethically.
The New Infrastructure Reality: Beyond Provider Lock-In
The fundamental value of cloud infrastructure isn’t diminishing—in fact, with AI workloads demanding unprecedented compute resources, the physical footprint of major providers becomes even more valuable. What’s changing is the level of provider-specific expertise required to leverage that infrastructure effectively.
The Multi-Cloud Opportunity
AI-powered operations are making multi-cloud strategies increasingly practical:
Workload Portability: Organizations can move applications between clouds with reduced friction
Best-of-Breed Selection: Companies can choose optimal providers for specific workloads
Cost Optimization: Customers can leverage price competition between providers more effectively
Risk Mitigation: Businesses can reduce dependency on any single provider
This doesn’t mean companies will abandon major cloud providers. Rather, they’ll be more selective about where different workloads run and more willing to distribute them across providers when advantageous. The infrastructure remains essential—what changes is the degree of lock-in.
The New Challenges: Emerging Demands on Cloud Operations
As operational advantages evolve, cloud providers face several converging forces that will fundamentally reshape traditional models. These emerging challenges extend beyond conventional scaling issues, creating qualitative shifts in how cloud infrastructure must be designed, managed, and secured.
The Vibe Coding Revolution
“Vibe coding” transforms development by enabling developers to describe problems in natural language and have AI generate the underlying code. This democratizes software creation while introducing different infrastructure demands:
Applications become more dynamic and experimental, requiring more flexible resources
Development velocity accelerates dramatically, challenging traditional operational models
Debugging shifts from code-focused to prompt-focused paradigms
As newer generations of developers increasingly rely on LLMs, critical security challenges emerge around software integrity and trust. The abstraction between developer intent and implementation creates potential blind spots, requiring governance models that balance accessibility with security.
Meanwhile, agentic AI reshapes application deployment through autonomous task orchestration. These agents integrate disparate services and challenge traditional SaaS models as business logic migrates into AI. Together, these trends accelerate cloud adoption while creating challenges for conventional operational practices.
The IoT and Robotics Acceleration
The Internet of Things is creating unprecedented complexity with over 30 billion connected devices projected by 2026. This expansion fragments the operational model, requiring seamless management across central cloud and thousands of edge locations. The boundary between edge and cloud creates new security challenges that benefit from AI-assisted operations.
Robotics extends this complexity further as systems with physical agency:
Exhibit emergent behaviors that weren’t explicitly programmed
Create operational challenges where physical and digital domains converge
Introduce security implications that extend beyond data protection to physical safety
Require real-time processing with strict latency guarantees that traditional cloud models struggle to address
The fleet management of thousands of semi-autonomous systems requires entirely new operational paradigms that bridge physical and digital domains.
The AI Compute Demand
AI training and inference are reshaping infrastructure requirements in ways that differ fundamentally from traditional workloads. Large language model training requires unprecedented compute capacity, while inference workloads demand high availability with specific performance characteristics. The specialized hardware requirements create new operational complexities as organizations balance:
Resource allocation between training and inference
Specialized accelerators with different performance characteristics
Cost optimization as AI budgets expand across organizations
Dynamic scaling to accommodate unpredictable workload patterns
These represent fundamentally different resource consumption patterns that cloud architectures must adapt to support—not simply larger versions of existing workloads.
The Security Imperative
As systems grow more complex, security approaches must evolve beyond traditional models. The attack surface has expanded beyond what manual security operations can effectively defend, while AI-powered attacks require equally sophisticated defensive capabilities. New security challenges include:
Vibe-coded applications where developers may not fully understand the generated code’s security implications
Robotics systems with physical agency creating safety concerns beyond data protection
Emergent behaviors in AI-powered systems requiring dynamic security approaches
Compliance requirements across jurisdictions demanding consistent enforcement at scale
Current cloud operations—even with elite human teams—cannot scale to these demands. The gap between operational requirements and human capabilities points toward AI-augmented security as the only viable path forward.
The Changing Competitive Landscape: A 5-10 Year Horizon
Over the next 5-10 years, these technological shifts will create significant changes in the cloud marketplace. While the timing and magnitude of these changes may vary, clear patterns are emerging that will reshape competitive dynamics, pricing models, and value creation across the industry.
Value Migration to Orchestration and Agentic Layers
Just as telecom saw value shift from physical networks to OTT services, cloud is experiencing value migration toward higher layers of abstraction. Value is increasingly found in:
Multi-cloud management platforms that abstract away provider differences
AI-powered operations tools that reduce the expertise barrier
Specialized services optimized for specific workloads or regulatory regimes
AI development platforms that facilitate vibe coding approaches
Agentic AI systems that can autonomously orchestrate tasks across multiple services
Hybrid SaaS/AI solutions that combine traditional business logic with intelligent automation
This doesn’t eliminate infrastructure’s value but alters competitive dynamics and potentially compresses margins for undifferentiated services. As Chuck Whitten noted regarding agentic AI’s impact on SaaS: “Transitions lead not to extinction but to transformation, adaptation, and coexistence.”
Increased Price Sensitivity for Commodity Services
As switching costs decrease through standardization and AI-powered operations, market dynamics shift significantly. We’re seeing:
Basic compute, storage, and networking becoming more price-sensitive
Value-added services facing more direct competition across providers
Specialized capabilities maintaining premium pricing while commoditized services face margin pressure
This creates a strategic landscape where providers must carefully balance commoditized offerings with differentiated services that address specific performance, security, or compliance requirements.
The Rise of Specialized Clouds
The market is evolving toward specialization rather than one-size-fits-all solutions. Three key categories are emerging:
Industry-specific clouds optimized for particular regulatory requirements in healthcare, finance, and government
Performance-optimized environments for specific workload types like AI, HPC, and real-time analytics
Sovereignty-focused offerings addressing geopolitical concerns around data governance and control
These specialized environments maintain premium pricing even as general-purpose computing becomes commoditized, creating opportunities for focused strategies that align with specific customer needs.
Salt Typhoon as a Cautionary Tale
The telecom industry’s commoditization journey reached a critical inflection point with the 2024-2025 Salt Typhoon cyberattacks. These sophisticated breaches targeted nine major US telecommunications companies, including giants like Verizon, AT&T, and T-Mobile, compromising sensitive systems and exposing metadata for over a million users. This crisis revealed how commoditization had led to chronic underinvestment in security innovation and resilience.
The aftermath was unprecedented: the FBI directed citizens toward encrypted messaging platforms as alternatives to traditional telecommunication—effectively steering users away from legacy infrastructure toward newer, more secure platforms. This government-endorsed abandonment of core telecom services represented the ultimate consequence of commoditization. Just as commoditization eroded telecom’s security resilience, cloud providers risk a similar fate if they grow complacent in an increasingly standardized market.
While cloud providers currently prioritize security more than telecom historically did, the Salt Typhoon incident illustrates the dangers of underinvestment in a commoditizing field. With innovation cycles compressed roughly 7-fold compared to telecom—meaning cloud technologies evolve at a pace telecom took decades to achieve—they have even less time to adapt before facing similar existential challenges. As AI agents and orchestration platforms abstract cloud-specific expertise—much like telecom’s reliance on standardized systems—security vulnerabilities could emerge, mirroring the weaknesses Salt Typhoon exploited.
Stakeholder Implications
The accelerating commoditization of cloud services transforms the roles and relationships of all stakeholders in the ecosystem. Understanding these implications is essential for strategic planning.
For Operations Teams
The shift from hands-on execution to strategic oversight represents a fundamental change in skill requirements. Engineers who once manually configured infrastructure will increasingly direct AI systems that handle implementation details. This evolution mirrors how telecom network engineers transitioned from hardware specialists to network architects as physical infrastructure became abstracted.
Success in this new paradigm requires developing expertise in:
AI oversight and governance
Cross-cloud policy management
Strategic technology planning
Risk assessment and mitigation
Rather than platform-specific implementation knowledge, the premium skills become those focused on business outcomes, security posture, and strategic optimization.
For Customers & End Users
The democratization of operational expertise through AI fundamentally transforms the customer’s role in the cloud ecosystem. Just as telecom users evolved from passive consumers of fixed telephone lines to active managers of their communication tools, cloud customers are transitioning from consumers of provider expertise to directors of AI-powered operations.
Enterprise teams no longer need specialized knowledge for each platform, as AI agents abstract away complexity. Decision-making shifts from “which cloud provider has the best expertise?” to “which orchestration layer best manages our multi-cloud AI operations?” This democratization dramatically reduces technical barriers to cloud migration and multi-cloud strategies, accelerating adoption while increasing provider switching frequency.
For Security Posture
The Salt Typhoon breach offers a sobering lesson about prioritizing efficiency over security innovation. The democratization of operational expertise through AI creates a paradox: security becomes both more challenging to maintain and more essential as a differentiator.
Organizations that can augment AI-driven security with human expertise in threat hunting and response will maintain an edge in an increasingly commoditized landscape. Without this focus, cloud providers risk becoming the next victims of a Salt Typhoon-scale breach that could potentially result in similar government recommendations to abandon their services for more secure alternatives.
For the Industry as a Whole
The drastic compression of innovation cycles means even foundational assets—massive infrastructure and deep operational expertise—face unprecedented pressure. Cloud providers must simultaneously integrate new AI capabilities while preserving their core strengths.
The rapid emergence of third-party orchestration layers is creating a new competitive battleground above individual clouds. This mirrors how over-the-top services disrupted telecom’s business model. Cloud providers that fail to adapt to this new reality risk following the path of telecom giants that were reduced to “dumb pipes” as value moved up the stack.
The Strategic Imperative: Evolution, Not Extinction
Cloud providers face a significant strategic challenge, but not extinction. The way forward requires evolution rather than entrenchment, with four key imperatives that can guide successful adaptation to this changing landscape. These strategies recognize that cloud’s value proposition is evolving rather than disappearing.
Embrace AI-Enhanced Operations
Providers that proactively integrate AI into their operational models gain significant advantages by:
Delivering higher reliability and security at scale
Reducing customer operational friction through intelligent automation
Focusing human expertise on high-value problems rather than routine tasks
Creating self-service experiences that democratize capabilities while maintaining differentiation
The competitive advantage comes not from simply adopting AI tools, but from reimagining operations with intelligence embedded throughout the stack—transforming how services are delivered, monitored, and optimized.
Lead the Multi-Cloud Transition
Rather than resisting multi-cloud adoption, forward-thinking providers are positioning themselves to lead this transition by:
Creating their own cross-cloud management capabilities
Optimizing for specific workloads where they excel
Developing migration paths that make them the preferred destination for critical workloads
Building partnership ecosystems that enhance their position in multi-cloud environments
The goal is becoming the strategic foundation within a multi-cloud strategy, rather than fighting against the inevitable trend toward workload distribution and portability.
Invest in Infrastructure Differentiation
Physical infrastructure remains a durable advantage when strategically positioned. Differentiation opportunities include:
Specialization for emerging workloads like AI
Optimization for performance characteristics that matter to key customer segments
Strategic positioning to address sovereignty and compliance requirements
Energy efficiency design in an increasingly carbon-conscious market
Architecture to support real-time processing demands of robotics and autonomous systems
Ultra-low latency capabilities for mission-critical applications
Infrastructure isn’t becoming irrelevant—it’s becoming more specialized, with different characteristics valued by different customer segments.
Develop Ecosystem Stickiness
Beyond technical lock-in, providers can build lasting relationships through ecosystem investments:
Developer communities that foster innovation and knowledge sharing
Education and certification programs that develop expertise
Partner networks that create business value beyond technical capabilities
Industry-specific solutions that address complete business problems
This ecosystem approach recognizes that relationships and knowledge investments often create stronger bonds than technical dependencies alone, leading to more sustainable competitive advantages over time.
The Path Forward: Three Strategic Options
Cloud providers have three strategic options to avoid the telecom commoditization trap as I see it right now:
Vertical integration into industry-specific solutions that combine infrastructure, expertise, and deep industry knowledge in ways difficult to commoditize. This approach focuses on value creation through specialized understanding of regulated industries like healthcare, finance, and government.
Specialization in emerging complexity areas where operational challenges remain high and AI assistance is still developing. These include domains like quantum computing, advanced AI training infrastructure, and specialized hardware acceleration that resist commoditization through continuous innovation.
Embracing the orchestration layer by shifting focus from infrastructure to becoming the universal fabric that connects and secures all computing environments. Rather than fighting the abstraction trend, this strategy positions providers at the center of the multi-cloud ecosystem.
Conclusion
Cloud providers face a clear choice, continue investing solely in operational excellence that is gradually being democratized by AI, or evolve their value proposition to emphasize their enduring advantages while embracing the changing operational landscape.
For cloud customers, the message is equally clear: while infrastructure remains critical, the flexibility to leverage multiple providers through AI-powered operations creates new strategic options. Organizations that build intelligence-enhanced operational capabilities now will gain unprecedented flexibility while potentially reducing costs and improving reliability.
The pattern differs meaningfully from telecom. While telecommunications became true commodities with minimal differentiation, cloud infrastructure maintains significant differentiation potential through performance characteristics, geographic distribution, specialized capabilities, and ecosystem value. The challenge for providers is to emphasize these differences while adapting to a world where operational expertise becomes more widely distributed through AI.
The time to embrace this transition isn’t in some distant future—it’s now. Over the next 5-10 years, the providers who recognize these shifts early and adapt their strategies accordingly will maintain leadership positions, while those who resist may find their advantages gradually eroding as customers gain more options through AI-enhanced operations.
The evolution toward AI-enhanced operations isn’t just another technology trend—it’s a significant shift in how cloud value is created and captured. The providers who understand this transformation will be best positioned to thrive in the next phase of cloud’s rapid evolution.
When selling security solutions to enterprises, understanding who makes purchasing decisions is critical to success. Too often, security vendors aim their messaging at the wrong audience or fail to recognize how budget authority flows in organizations. This post tries to break down the essential framework for understanding enterprise security buyer dynamics.
While this framework provides a general structure for enterprise security sales, industry-specific considerations require adaptation. Regulated industries like healthcare, finance, and government have unique compliance requirements, longer approval cycles, and additional stakeholders (e.g., legal, risk committees).
The Buyer Hierarchy
The first key concept to understand is the buyer hierarchy in enterprise security.
Figure 1: The Buyer Hierarchy
This pyramid structure represents who typically makes purchasing decisions at different price points:
At the base of the pyramid are Security and IT Managers. These individuals make most purchase decisions, particularly for:
Standard solutions with established budget lines
Renewals of existing products
Smaller ticket items
Solutions addressing immediate operational needs
Moving up the pyramid, we find Security and IT Directors who typically approve:
Larger deals requiring more significant investment
In security sales, it’s crucial to distinguish between two key players:
The Champion: This person is chartered to solve the problem. They’re typically your main point of contact and technical evaluator – often a security engineer, DevOps lead, or IT admin. They’ll advocate for your solution but rarely control the budget.
The Buyer: This is the person who owns the budget. Depending on the size of the deal, this could be a manager, director, or in some cases, the CISO. They make the final purchasing decision.
Understanding this dynamic is critical. Too many sales efforts fail because they convinced the champion but never engaged the actual buyer.
The Budget Factor
Another critical dimension is whether your solution is:
Pre-budgeted: Already planned and allocated in the current fiscal year
Unbudgeted: Requires new budget allocation or reallocation from other initiatives
Figure 2: Budgetary Timing Diagram
This distinction dramatically impacts who needs to approve the purchase. Unbudgeted items almost always require higher-level approval – typically at the CISO level for any significant expenditure, as they have the authority to reallocate funds or tap into contingency budgets.
The Cross-Organizational Challenge
A critical dimension often overlooked in enterprise security sales is cross-organizational dynamics.
When security purchases span multiple departments (e.g., budget from Compliance, implementation by Engineering), the buyer hierarchy becomes more complex. Moving funds between departmental budgets often requires executive approval above the standard buyer level.
Different departments operate with separate success metrics, priorities, and approval chains. What solves one team’s problems may create work for another with no benefit to their goals. These cross-organizational deals typically extend sales cycles by 30-50%.
For vendors navigating these scenarios, success depends on mapping all stakeholders across departments, creating targeted value propositions for each group, and sometimes elevating deals to executives who can resolve cross-departmental conflicts.
The Cost of Sale Framework
As solutions become more enterprise-focused, the cost of sale increases dramatically.
Figure 3: Cost of Sale Diagram
This framework illustrates a critical principle: The cost of sale must be aligned with the buyer level.
For solutions with a higher cost of sale (requiring more sales personnel time, longer sales cycles, more supporting resources), vendors must sell higher in the organization to ensure deal sizes justify these costs.
Key components affecting cost of sale include:
Sales personnel salary
Number of accounts per sales rep
Sales cycle length
Supporting resources required
This explains why enterprise security vendors selling complex solutions must target the CISO budget – it’s the only way to recoup their significant cost of sale.
Relationship Dynamics and Timing Considerations
While understanding the buyer hierarchy is essential, most successful enterprise security deals don’t happen solely through identifying the right level in an organization.
Figure 4: Cost of Sale Diagram
Two critical factors often determine success:
Relationship Development: Successful sales rarely happen in a transactional manner. They require:
Building trust through consistent value delivery before the sale
Understanding the internal politics and relationships between champions and buyers
Developing multiple organizational touchpoints beyond just the champion
Recognizing the personal career motivations of both champions and buyers
Timing Alignment: Even perfect solutions fail when timing is wrong:
Budget cycle alignment is critical – engage 3-6 months before annual planning
Crisis or incident response periods can accelerate purchases or freeze them
Organizational changes (new leadership, restructuring) create both opportunities and risks
Regulatory deadlines often drive urgent security investments
The most effective security vendors don’t just target the right level in the hierarchy – they strategically time their engagements and invest in relationship development that transcends organizational charts.
Practical Application
For security vendors, this framework provides practical guidance:
Know your buyer level: Based on your solution’s price point and complexity, identify your primary buyer persona (Manager, Director, or CISO)
Target champions appropriately: Ensure your technical messaging resonates with the people who will evaluate and champion your solution
Align marketing to both: Create distinct messaging for champions (technical value) and buyers (business value)
Understand the budget cycle: Time your sales efforts to align with budget planning for better success with larger deals
Match sales approach to cost structure: Ensure your go-to-market approach and resources match your cost of sale
By aligning your sales and marketing efforts with these buyer dynamics, you’ll significantly improve your efficiency and close rates in the enterprise security market.
Ever been in a meeting where someone drops terms like “TEE,” “TPM,” or “FIPS-certified” and everyone nods along, pretending they understand? Yeah, me too.
“Some discussions would be so much easier if people knew the definitions of ‘TEE’, ‘TPM’, ‘Secure element’, ‘Secure enclave’, ‘HSM’, ‘Trusted computing’, ‘FIPS(140-2/3)-certified’, ‘Common criteria’, ‘security target’, etc. Plus now the marketing-oriented term ‘confidential computing’ is used to mean a variety of things with varying security properties.”
He’s right – the security tech space is a mess of overlapping terms, marketing buzzwords, and genuine technical concepts. So I threw together a guide to sort this stuff out.
What’s Actually Different Between These Things?
At their core, these technologies do three things:
Minimize what code you need to trust (the TCB)
Create isolation between different parts of a system
Establish trust across different machines
A TPM is not the same as a TEE. Intel SGX is not identical to AMD SEV. And no, slapping “FIPS-certified” on your product doesn’t automatically make it secure.
The Real-World Impact
When your vendor says they use “Confidential Computing,” do you know what that actually means for your data? Could be anything from “your data is encrypted in memory” to “we’ve got a fancy marketing term for standard virtualization.”
The differences matter. A secure element in your phone has around 10-50KB of trusted code. A standard Linux kernel? About 27.8 MILLION lines. One of these is much easier to secure than the other.
When Things Break
Even the most certified security tech fails. Hardware Security Modules (HSMs) with FIPS 140-2 certification—supposedly the gold standard for cryptographic security—have been compromised by design flaws. Look at the 2015 Safenet HSM vulnerability where API flaws in the PKCS#11 interface allowed full key extraction. Attackers with authenticated access could exploit weak key derivation mechanisms to extract the very keys the HSM was designed to protect.
Bottom line: No security technology is perfect. Each has its place, limitations, and potential failure modes.
As Winston Churchill observed, “He who fails to plan is planning to fail.” Understanding what’s under the hood of these technologies isn’t just academic—it’s essential for building systems that can actually withstand the threats they’ll face.
The evolution of technology operations has always been driven by necessity. From the early days of single system operators (sysops) managing physical servers through hands-on intervention, to today’s complex landscape of distributed microservices, containers, and serverless functions, each operational paradigm shift has emerged to address growing complexity.
The Journey of Operational Evolution
From the hands-on Sysops era of the 1960s-80s when operators physically managed as as little as few to 10s of servers each, to the System Administration period of the 1990s when centralized tools expanded reach to hundreds of systems, technology operations have continuously transformed. DevOps emerged in the mid-2000s, leveraging Infrastructure as Code to manage thousands of systems, followed by SRE practices in the 2010s with error budgets and self-healing systems handling tens of thousands of containers. Looking ahead to 2025, AI-Driven Operations promises autonomous management of millions of components.
Each transition has been driven by necessity – not choice – as technology’s relentless complexity has overwhelmed previous operational models.
The Machine Concept Has Transformed
What’s particularly interesting is how we use the word “machine” has changed dramatically. In the early days, machines were physical servers with stable operating systems and predictable maintenance schedules. Today, with serverless computing, the very concept of a server has become fluid – functions materialize only when triggered, often lasting mere seconds before vanishing.
This ephemeral nature of modern computing creates unprecedented coordination challenges that exceed manual and even moderate automation approaches to management.
The Limits of Current Approaches
Even advanced DevOps and SRE practices are struggling with the scale and complexity of today’s systems. Many vendors have responded by adding AI or ML features to their products, but these “bolt-on” enhancements only provide incremental benefits – analyzing logs, detecting anomalies, or generating suggestions for known issues.
What’s needed is a more fundamental reimagining of operations, similar to how cloud-native architectures transformed infrastructure beyond simple virtualization.
AI-Native: A New Operational Paradigm
An AI-native platform isn’t just software that applies ML algorithms to operational data. It’s a new foundation where intelligence is deeply integrated into orchestration, observability, security, and compliance layers.
In these systems:
Instrumentation is dynamic and context-aware
Security is adaptive, learning normal communication patterns and immediately flagging and in even some cases quarantining anomalous processes
Compliance shifts from periodic audits to continuous enforcement
The timeline above illustrates how each operational era has enabled engineers to manage exponentially more systems as complexity has grown.
This diagram shows the widening gap between human management capacity and system complexity, which AI-native operations will ultimatley address.
The Human Role Transforms, Not Disappears
Rather than eliminating jobs, AI-native operations redefine how engineers spend their time. As a result, we will ultimately see the concept “force multiplier engineers” who will build advanced AI-driven frameworks that amplify the productivity of all other developers.
Freed from repetitive tasks like scaling, patching, and log parsing, these professionals can focus on innovation, architecture, and strategic risk management.
The Inevitable Shift
This transition isn’t optional but inevitable. As systems become more fragmented, ephemeral, and globally distributed, conventional approaches simply can’t keep pace with the complexity.
Those who embrace AI-native operations early will gain significant advantages in reliability, security, cost-efficiency, and talent utilization. Those who hesitate risk being overwhelmed by complexity that grows faster than their capacity to manage it.
What do you think about the future of AI in operations? Are you seeing early signs of this transition in your organization? Let me know in the comments!
