Anton Chuvakin

(written jointly with Tim Peacock Five years. It's enough time to fully launch a cloud migration, deploy a new SIEM, or — if you're a very

You read the “AI-ready SOC pillars” blog , but you still see a lot of this: How do we do better?

OK, RSA 2026 is over. If my record keeping is correct, I first attended RSA in 2006 . At that time, I was annoyed by … AI? XDR?

Breaking the Patch Sound Barrier: Your Vulnerability Remediation Will Not Keep Up With AI Exploit Speed. So?

So, you read my previous blog post about breaking the patch sound barrier, but it left you wanting more? Well, this is that “more.”Gemini blog illustration / steampunk vuln apocHere are three useful ideas to advance the conversation.1. Defining the “Vulnerability Apocalypse”People love to throw around terms like vulnerability apocalypse, but what does it actually mean? What is the crisp definition? Here:Anton’s Vulnerability Apocalypse (VulnPocalypse) is …… a rapid step increase in:1. The number of software vulnerabilities (including zero-days i.e. vulnerabilities not known to defenders),2. Speed of exploit development,3. Volume of exploitation based on them,4. Resulting incident damage.With some help from the fine folks on Twitter and LinkedIn — and Gemini, naturally — this is what I got.Note that for a situation to truly qualify as “an apocalypse”, all four of these factors must be present simultaneously:Massive Volume: A staggering influx of new vulnerabilities.Rapid Exploit Development: Attackers weaponizing flaws nearly immediately.Evident Exploitation: AI and automated tools scanning and exploiting at scale.Severe Incident Damage: Widespread, material business impact resulting directly from these compromises.Let me highlight that fourth factor: incident damage. If you have a massive spike in vulnerabilities, but it doesn’t result in actual, widespread damage, it isn’t an apocalypse — it’s just a high-volume vuln Tuesday.How do we track that this is indeed coming?2. The Polarization of “Patch Faster”Ever since advanced models capable of hunting down vulnerabilities emerged, the traditional advice of “just patch faster” has become incredibly polarizing. Ultimately, my take aligns closely with a recent Cloudflare post: “Patching faster does not change the shape of the pipeline that produces the patch. If regression testing takes a day, you cannot get to a two-hour SLA without skipping it, and the bugs you ship when you skip regression testing tend to be worse than the bugs you were trying to patch.”So, yes, do patch faster. And, no, patch faster won’t save you.What will? This!3. A Thought Experiment: The 15-Minute Magic WandLet me leave you with a useful thought experiment I recently used in a presentation.Imagine you wake up tomorrow morning and, by pure force of magic, any vulnerability in your systems, applications, and operating systems can be patched within 15 minutes of patch release. The dream has come true!Now for the fun part: Reverse engineer that reality.What fundamental changes had to happen in your environment to make that 15-minute window physically possible?If you actually run through this exercise, you will discover a goldmine of hidden opportunities. You’ll identify exactly where you can streamline software updates, eliminate legacy roadblocks, and modernize your architecture.Will it actually get your entire enterprise to a 15-minute patch cycle? No, probably not — and definitely not for every legacy application. But it will give you a concrete, actionable roadmap for modernizing your IT.Let’s hope this was both fun and useful.Related blog:Breaking the Patch Sound Barrier: Your Vulnerability Remediation Will Not Keep Up With AI Exploit Speed. So?Breaking the Patch Sound Barrier Part 2: So Is The Apocalypse Coming and What Is It? was originally published in Anton on Security on Medium, where people are continuing the conversation by highlighting and responding to this story.

One particular aspect of an agentic or AI-powered SOC (but NOT “humanless SOC”) has bothered me over the last few months: specifically, the people and process side of such a SOC. If you recall my blog posts (part 1, part 2 and this video) about AI SOC readiness, I hinted at certain elements of a traditional process stack and legacy personnel profiles (both technical and leadership) that make AI adoption inside SOC incredibly difficult.So we (me and Augusto Barros @ Prophet Security) want to create a modernized people and process framework for a SOC powered by AI and intelligent agents. Otherwise, what I am observing is a lot of “robotic horse pulls a buggy” kind of operations — where everything is kept exactly the same as it was in 2003, but “AI SOC” tools are simply tacked on to perform some of the tasks.Gemini visual of old SOC with “AI SOC” toolsI believe that people and process components must change far more dramatically, and such changes are a critical requirement for achieving “step change” SOC with AI capabilities. Simply adding AI tools and Ai agents to a 2003-style SOC will produce, at best, marginal results. Things would get better, but not better enough to counter the feared “bad guy with AI.”The SOAR AnalogyThe analogy I want to use here is SOAR adoption from 10+ years ago. Back then, organizations simply shifted a few processes — or even just specific tasks — to a machine, and then kept the rest of their operations exactly the same. Because of that, I observed a lot of SOAR tools being used strictly for alert enrichment or for dealing with one specific, isolated type of alert, like phishing. To follow this analogy to the present day, I now frequently see an “AI SOC” being utilized only for EDR alerts or only for phishing alerts (wow, what a coincidence!)A First-Principles ApproachWhat I really want to build is a first-principles approach to the specific personnel, skills, processes, and practices required to run a true agentic SOC in the late 2020s.Now, if you prefer incremental change, that is OK, I won’t judge. However, you must be aware that the same principles caused organizations to struggle with cloud adoption. People often hear that “lift and shift” is bad. Most consultants will tell you that “lift and shift” is fine as a first step, but you eventually need to modernize and take more steps. Unfortunately, many organizations never make that second step. The same risk applies to the AI SOC. 2003 SOC + AI = somewhat better 2003 SOC.BTW, many artifacts of the modern, engineering-powered SOC — which we covered in our now-famous ASO (Autonomic Security Operations) paper back in 2021s — apply here as well. In fact, if you recall, one of our core principles was: Humans build machines; machines do the work.In the context of an agentic SOC, that evolves into:Today, humans build the machines with the help of other machines, and then the machines do the heavy lifting.So, our questions so far:What do humans do in an agentic SOC?What do entry-level humans do?What SOC processes stay the same despite AI?What SOC processes can just go and vanish (triage)?What processes get handed to machines?Are there new processes for humans?What is the new human role for validation?How do we check AI quality without fully redoing the work?How SOC metrics must change due to AI and agents? (some ideas)What do humans and machines do jointly? What does it mean, practically?How to HITL in a SOC without breaking the humans or machines?What is the effective mechanism for the human-to-AI feedback loop so that corrections actually improve future SOC performance?Is “fully automated” detection engineering a realistic goal, or does the dependency on local, inconsistent environment context make it inherently a hybrid human-machine effort?What do humans do before SOC (TI) and after SOC (IR)?What is the first step to move from a legacy SOC to an agentic SOC?Can we run legacy and agentic SOC structures in parallel during transition, or does this duplication create operational friction?Is it easier to move from a modern non-AI SOC (aka “SOCless D&R”) to an AI SOC?Looking AheadThis blog post is just the first part of the series. My goal here is simply to collect the right questions we need to be asking, but I promise we will provide concrete answers in upcoming posts. This research is being undertaken together with my former colleague, Augusto Barros, now at Prophet SecurityRelated blogs:Beyond “Is Your SOC AI Ready?” Plan the Journey!Simple to Ask: Is Your SOC AI Ready? Not Simple to Answer!WTH is Modern SOC, Part 1Baby ASO: A Minimal Viable Transformation for Your SOCBeware: Clown-grade SOCs Still AboundEP264 Measuring Your (Agentic) SOC: Two Security Leaders Walk into a PodcastNew Paper: “Future of the SOC: Evolution or Optimization — Choose Your Path” (Paper 4 of 4.5)Stop Building a 2003 SOC with AI: A Modern People & Process Framework (Part 1) was originally published in Anton on Security on Medium, where people are continuing the conversation by highlighting and responding to this story.

Customise Consent Preferences

For 15 years (!), many of us who have touched cloud security have struggled with the shared responsibility model for cloud security. As with many “cyber things,” the theory is simple. Multiple vendors, consulting firms, and industry bodies have published deceptively clear matrices that depict exactly who is doing what for cloud security.Everyone likes to present trivial cases: for example, the cloud provider is entirely responsible for the physical security of the data center, while the client is responsible for the application they just built and deployed within that cloud provider’s IaaS. In reality, many of the edge cases continue to cause pain to a lot of organizations.Ok, so none of this is fundamentally new. However, in recent years, similar and more complex - dare I say sinister?- questions have emerged: What does shared responsibility look like for AI security?Those who haven’t studied this topic in depth might assume there is no difference. Yet, there are fascinating, critical differences between shared responsibility for AI security and traditional cloud security, along with older related challenges like the shared security of outsourcing (that predate cloud).Add AI with its probabilistic behaviors, untrusted user inputs, and nested vendor dependencies -and that finger-pointing cycle doesn’t just continue, it scales exponentially. When a customer-facing chatbot goes off the rails, the model provider blames your prompt engineering, the platform provider claims infrastructure isolation worked perfectly, and your internal application team swears it’s an upstream model limitation…Put simply, what are the top 3 differences between shared responsibility for AI vs cloud? In my opinion:A Broader Spectrum of Risk: The range of harms and risks we must consider is much wider. Shared responsibility for AI security frequently touches upon safety, privacy, ethical use, and the unique risk surfaces that emerge specifically in conversations about AI.The Multi-Party Supply Chain: AI security is typically far more multi-party than traditional cloud security. For instance, one company builds the foundational model, another company fine-tunes it, a third party builds a Retrieval-Augmented Generation (RAG) architecture for you, and yet another party builds the consumer-facing application.Non-deterministic Behavior: Unlike traditional cloud infrastructure where secure configurations yield predictable, deterministic outcomes, AI systems are non-deterministic. Because outputs can vary significantly based on user inputs, customers bear increased responsibility for implementing robust guardrails, continuous monitoring, and input/output filtering.Early attempts to create a logical foundation for AI shared security responsibility produced some answers — and more questions.In light of this being a tricky problem, here I really want to focus on one thing — a post-incident scenario. While shared responsibility covers numerous use cases (and numerous sources of confusion…), let’s examine a fairly straightforward situation: I am an enterprise end-user company that uses (maybe builds, maybe tunes, etc) AI in some form, then something blows up (digitally, as this is not IoT/ICS security blog). So:Who takes a loss vs who is to blame?Do I blame the model creator? The application developer? The model hosting platform? Or do I ultimately blame myself?If you recall, many early challenges with the cloud shared responsibility model began with customers trying to blame the provider, only to discover they were actually at fault in the end. We tried to change this dynamic by introducing a “shared fate” model. While that specific terminology has seemingly fallen out of favor lately, the underlying philosophy remains: providers can probably do more to make AI usage inherently secure.I was recently involved with a CoSAI (Coalition for Secure AI) working group to develop a paper covering the shared responsibility framework for AI security. As others on the team humorously pointed out, my voice was one of the loudest calling for the paper to be kept simple, crisp, and highly usable. You can judge based on the final result whether we succeeded.CoSAI matrixWe recently wrapped up and approved Version 1.0 of the CoSAI AI Shared Responsibility Framework (AI SRF) through the Coalition for Secure AI and OASIS Open. The core mission here wasn’t to build more abstract compliance theater, but to solve a practical, glaring operational pain point: Who actually owns what when an AI system fails?Under the CoSAI framework, accountability traces down the stack with absolute clarity:The AI Model Provider (L5) is accountable for the base model’s inherent susceptibility to prompt injection and must document those boundaries explicitly within the model card.The Cloud/Platform Provider (L4) is accountable for the blast-radius containment, ensuring infrastructure-level tenant process isolation held firm during the exploitation.The Application Developer (L3) is accountable for failing to enforce application-level guardrails, input filtering, and localized data access controls that allowed the chatbot to hit the PII repository in the first place.The Deploying Organization (L1/L2) is accountable for the ultimate governance failure: they did not properly classify the data or restrict the chatbot’s system-level access boundaries before pushing it live.LayersAlso, the paper included a phased Implementation Playbook in the document to give security teams a somewhat specific path forward:Phase 1 (Days 1–30): Map your entire AI system inventory and cross-reference vendor contracts against these five layers to highlight immediate responsibility gaps.Phase 2 (Days 31–90): Establish a cross-layer governance committee and formally update vendor procurement contracts with clean, explicit accountability matrices.Phase 3 (12 Months): Run layer-specific tabletop simulations to stress-test your incident response playbooks before an actual threat actor tests them for you.Fun quotes:“Ambiguous ownership is a growing liability for Al system deployments.” “Without explicitly assigned owners for detection, containment, and remediation, teams default to the finger-pointing cycle” “The framework turns ‘whose fault is this?’ into ‘which layer’s controls failed, and who owns remediation for each?’” “There should be exactly one accountable party per component to prevent overlaps.” “Clear accountability eliminates finger-pointing during incidents” More seriously, read the paper!In the end, I hope this work enlightens people on just how complex this problem truly is. This paper is definitely not a silver bullet that solves everything overnight; we have years of discussions and evolving challenges ahead of us down this path. However, I think this paper serves as an excellent first step. Please make sure to check out the resources listed at the end of the paper as well (a lot of gems there!)Related blogs:Who’s Responsible When AI Goes Wrong? A New Framework Aims to Answer That QuestionWhere Does Shared Responsibility Model for Security Breaks in the Real World?No Snow, No Flakes: Pondering Cloud Security Shared Responsibility, Again!EP203 Cloud Shared Responsibility: Beyond the Blame Game with Rich MogullEP145 Cloud Security: Shared Responsibility, Shared Fate, Shared Faith?Who Does What In Cloud Threat Detection?The Cloud Shared Irresponsibilities ModelFrom Cloud to Chaos: Defining Shared Responsibility for AI Security was originally published in Anton on Security on Medium, where people are continuing the conversation by highlighting and responding to this story.

If you look at social media debates about AI, two extreme patterns emerge. Studying extreme patterns is very useful because understanding boundary