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:

1. 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.
2. 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.
3. 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.

We 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.

Also, the paper included a phased Implementation Playbook in the document to give security teams a somewhat specific path forward:

1. 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.
2. Phase 2 (Days 31–90): Establish a cross-layer governance committee and formally update vendor procurement contracts with clean, explicit accountability matrices.
3. 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.” [A.C. — filed under ‘no shit, Sherlock’]
• “Without explicitly assigned owners for detection, containment, and remediation, teams default to the finger-pointing cycle” [A.C. — this will get worse, then MUCH worse, then eventually better…]
• “The framework turns ‘whose fault is this?’ into ‘which layer’s controls failed, and who owns remediation for each?’” [A.C. — this is beautifully, I probably wrote this :-)]
• “There should be exactly one accountable party per component to prevent overlaps.” [A.C. — ideal world called, it wants its problem back! Real world picked up and said ‘get lost’]
• “Clear accountability eliminates finger-pointing during incidents” [A.C. — clear evidence that Captain Obvious is alive!]

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 Question
• Where 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 Mogull
• EP145 Cloud Security: Shared Responsibility, Shared Fate, Shared Faith?
• Who Does What In Cloud Threat Detection?
• The Cloud Shared Irresponsibilities Model

From 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.

Intezer already investigates 100% of your alerts and escalates fewer than 2% of them for human review. That part is handled.

The work does not stop there, though. Every SOC has its own routines wrapped around the investigation itself. The incident reports written in a particular format, the closure notes, the shift handoffs, the rules that decide who picks up which case. When we looked at how teams actually use AI Chat, our in-product investigation agent, more than a third of those conversations turned out to be the same repetitive tasks asked again and again. The same summaries. The same reports. The same closure notes.

Intezer’s AI SOC already runs agents around the clock to triage, investigate, and respond to your alerts on their own. Custom Agents is the next step. Now you can shape how that AI SOC works for your team. Add your own agents and automations on top of the ones Intezer runs out of the box, take more of the manual work off your analysts, and tailor the whole thing to the way your team actually operates.

Meet Custom Agents

Intezer ships with a set of agents and automations that handle triage, investigation, and response from day one. Custom Agents lets you build your own on top of them.

An agent is made up of three components:

1. Your instructions
2. A trigger
3. The tools it is allowed to use

You describe what you want done in plain language, choose when it should run, and pick what it can touch. It then runs on its own inside your Intezer environment, on the same engine that powers our investigation Agent (Chat).

Build an agent in minutes

1. Tell it what to do, in plain language. Write the instructions the way you would brief a new analyst. “Every morning, review the open case queue, close the clear false positives per our playbook, and leave a handoff note on the rest.” That is an agent.

2. Choose when it runs. Three trigger types cover most workflows:

• On a schedule: every day, week, or month. For example, a 9:00 report.
• On an event: the moment a case is closed, a verdict is set, or an alert meets your conditions.
• On demand: run it yourself, or call it from the API.

3. Give it the right tools. Agents work across your whole stack which includes Intezer’s built-in toolset plus the SIEM, EDR, and identity tools you have already connected, including CrowdStrike, SentinelOne, Splunk, Microsoft Sentinel, and Entra ID. They do more than summarize. They take action by updating, commenting on, closing cases, and emailing a finished report to your team.

See it in action

Take an Incident Report Writer agent illustrated above. We deliberately never shipped a single “Generate report” button, because no two teams want the same report. One team wants an executive summary up top, another wants the full timeline, another has a compliance format it has to match. So instead of a button, you put your format into the agent’s instructions, and it writes every report that way, every time.

The agent triggers on every escalated case an analyst has confirmed as a real threat. It reads the case, writes the report in your format, and emails it to your team’s inbox. The analyst makes the call. The paperwork writes itself.

That’s one agent. The point of Custom Agents is that you decide what they are.

Nothing runs blind

Security teams do not trust black boxes, and they are right not to. Custom Agents is built so you can see and control everything an agent does.

• Every run is visible. You see the agent’s reasoning, every tool call and its result, and the final output. Each run is logged, and you can export it.
• Test safely with Dry Run. Dry Run executes the agent for real but mocks every write action, so you can watch exactly what it would do before it does anything. Iterate on the instructions until it is right, then turn it on.
• Guardrails are built in. Action tools are limited by design. An agent can only email active members of your organization, for example. It cannot reach outside your walls.
• You stay in control. You choose which tools an agent gets, you review its output, and you can switch it off in one click.

This is how everything at Intezer works. AI executes, humans supervise. Custom Agents lets you decide what it executes.

What security teams are already building with it

We opened Custom Agents to a small group of alpha customers, and the best part has been watching what they build. Alongside the Incident Report Writer above, a few of the agents already running in production:

• SLA Monitor (daily): a morning email listing every escalated case that has been sitting too long, so nothing critical slips past its deadline.

• Tuning Advisor (weekly): takes the alerts your detection tools fired that Intezer judged to be false positives and turns them into suppression recommendations for the week ahead.

• Threat Hunter (weekly): proactively sweeps your environment for the latest threats instead of waiting for an alert to fire. It pulls the new malware families, campaigns, and indicators Intezer is tracking, queries your connected SIEM and EDR for matches across historical data, and opens a case for anything it surfaces.

• Smart Triage and Routing: for organizations with multiple entities, subsidiaries, and stakeholders, the agent reads each case and works out which team should own it, using your own escalation rules. It either leaves a comment with the routing, or assigns the case to the right analyst directly. Analysts stop digging through a separate list or knowledge base to figure out where a case goes.

• End of Shift Handoff: built to match what a real SOC handover looks like. At the end of a shift the agent compiles the open items, the escalations still waiting for attention, the shift’s statistics, and any open system events or configuration issues, then writes the handoff so the next shift starts with the full picture.

The AI SOC, built for your team

We are on a mission to build the AI SOC the industry has been promised but never delivered. One that does the work and earns the trust to do it. It runs autonomously, around the clock. It works alongside the people who supervise it, not over their heads. And it is never a black box. You can always open it up, question what it did, and change how it behaves.

Custom Agents is central to that vision. Triage, investigation, and response come built in. Everything particular to how your team operates, you build yourself. Because the strongest security teams have always run on their own playbooks, their own logic, and their own standards, and an AI SOC should be no different. It should not ship the same to everyone. No two SOCs are the same, and no two should be.

That is the point of Custom Agents. You decide what they are.

Available now

Custom Agents is available now in beta to Intezer customers, and it is free during the beta period. This is the moment to build, test, and tell us what you want it to do next.

See what your SOC could hand off. Book a demo.

If you are already an Intezer customer, you will find it under Custom Agents in the top menu.

The post Introducing Custom Agents: Automate your SOC, your way appeared first on Intezer.

Attackers can exploit LLM domain hallucinations through phantom squatting to target supply chains. Read the analysis to learn more.

The post Phantom Squatting: AI-Hallucinated Domains as a Software Supply Chain Vector appeared first on Unit 42.

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Unmasking the Digital Trail: Essential Techniques for Vetting AI-Generated Content

In our latest on-demand webinar, we outline the practical, human-driven techniques threat intelligence teams must deploy to detect synthetic media, protect corporate RAG ecosystems, and filter through the noise of AI-polluted networks.

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Flashpoint

June 29, 2026

Table Of Contents

Table of Contents

Neutralizing the Automated RAG and Vector Database Trap

Triangulating AI Flaws and Anomalies Across Modalities

Empowering the Human Layer | Watch the Full Webinar

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In the era of generative artificial intelligence (AI), threat intelligence is facing a profound signal-to-noise challenge. AI has introduced a massive paradigm shift to threat actor operations—making execution extremely easy while simultaneously dramatically complicating the task of verification for security teams.

In our latest on-demand webinar, Matt Edmonson, SANS Senior Instructor and founder of Argelius Labs, joined Flashpoint to discuss the intersection of Open Source Intelligence (OSINT) and AI. Drawing from his vast federal law enforcement experience, he shared actionable, human-driven techniques for detecting and vetting AI-generated online content.

Neutralizing the Automated RAG and Vector Database Trap

Before deploying any human-driven vetting techniques, an analyst must understand the specific structural trap threat actors are laying. Adversaries are no longer just using AI to spin up isolated phishing copy; they are using it to corrupt the automated defense pipelines that security teams rely on.

Modern threat intelligence workflows utilize automated ingestion to feed open-source data directly into local vector databases and Retrieval-Augmented Generation (RAG) models. Aware of this, sophisticated threat actors deploy a coordinated infrastructure strategy: they register multiple lookalike domains simultaneously to broadcast the exact same AI-generated disinformation narrative.

When automated security tools ingest this data, the system flags multiple distinct “sources” confirming the story as truth. This structural echo chamber completely bypasses automated verification safeguards, polluting corporate databases with validated lies. We have seen this play out via:

• Long-Game Credibility Building: Edmonson highlighted an active Foreign Malicious Influence (FMI) campaign utilizing a French lookalike news site called Verite Cache (“The Hidden Truth”). The threat actors scrape legitimate Western news, use AI to rewrite it to build structural domain authority over time, and then manipulate narrative outcomes the moment a critical geopolitical event or election occurs.
• Simultaneous Infrastructure Deployment: This pattern was mirrored in Southeast Asia, where Singapore recently banned six lookalike news sites targeting regional discourse. Upon technical inspection, five of those six distinct domains had been registered on the exact same day to broadcast a unified narrative.
• Organic-Looking Algorithmic Surges: The scale of these operations can shift political landscapes in a matter of days. Romania recently took the extreme step of canceling and restarting its presidential election due to a covert, highly coordinated Russian-backed social media campaign. The operation used synthetic assets to trigger algorithmic recommendation engines, driving an intense, seemingly organic surge for an underdog candidate.

Triangulating AI Flaws and Anomalies Across Modalities

Vetting AI content relies on compiling a cluster of intersecting indicators across text, images, audio, and video until a definitive analytical confidence level is reached. While generative tools have grown highly sophisticated, they are still bound by mathematical constraints and architectural limitations. Catching these errors and inconsistencies requires analysts to identify a cluster of intersecting indicators across text, images, audio, and video:

• Textual Analytics (Linguistic Quirks and Filler Text): Large Language Models (LLMs) leave distinct behavioral footprints. Analysts should look for commonly-used AI wordings and “portable sentences”, as well as automated translation leakage that reveals a threat actor’s native language mechanics.
• Visual Logic Flaws (Physics and Seams): AI models frequently fail to grasp the fundamental physics of the real world. Analysts should closely inspect image logic for anatomical blunders (such as inverted hand structures), impossible geometry, or objects with extreme structural flaws. Additionally, AI struggles with “texture seams”—the exact boundaries where distinct textures meet.
• Auditory and Video Glitches (Cadence and Duration): Human speech is inherently messy, characterized by breathing pauses, environmental background noise, and shifting cadences. Synthetic speech is often locked into a perfectly uniform, monotone rhythm. Furthermore, high-fidelity deepfakes are incredibly resource-intensive to sustain over long durations. While an actor can fake 10 to 15 seconds of synthetic video convincingly, a five-minute video will almost always display jarring cuts, visual artifacting, or avatars clipping out of frame.

Empowering the Human Layer | Watch the Full Webinar

Human analysts remain the most critical layer of defense against illicit uses of AI. Empowered by comprehensive threat intelligence, OSINT, and AI technologies, security teams can hunt for clusters of intersecting indicators across text, images, audio, and video to assess authenticity. To learn more and to gain more essential techniques, watch the full on-demand webinar. Using Flashpoint, organizations can filter through noise, execute critical data premortems, and neutralize sophisticated disinformation campaigns.

The post Unmasking the Digital Trail: Essential Techniques for Vetting AI-Generated Content appeared first on Flashpoint.

Unit 42's analysis of ClawHub revealed evasive malicious skills bypassing automated scanners to deploy infostealers and execute agentic financial fraud.

The post OpenClaw’s Skill Marketplace and the Emerging AI Supply Chain Threat appeared first on Unit 42.