AI agent identity answers who this is and whether it's allowed in. It does not answer whether what it did was okay. This guide covers what agent identity is, why traditional IAM breaks for agents, the delegation-versus-impersonation decision, the four-layer identity model, seven identity mechanisms compared by the audit log line each one leaves behind, how identity failed in the ServiceNow BodySnatcher case, and what identity cannot solve on its own.
AI Agent Accountability: Who's Responsible When It Acts?
Adesh Gairola ·
Four parties can be accountable when an AI agent takes the wrong action: the deploying organisation, the permission grantor, the agent vendor, and the model provider. Which one it lands on is decided by evidence, not by contract language, and the single field most logs are missing, an actor distinct from the human whose credential the agent borrowed, is enough to collapse every one of those claims at once.
10 things to assume before you run GPT-6 Astra or Claude Fable 5.1 in production
Adesh Gairola ·
GPT-6 Astra and Claude Fable 5.1 shipped this month. If you plan to run them in production, some of the risk in their own system cards is yours to accept rather than mitigate. This is a walk through which parts.
A deterministic decision point outside the model, in the live path, that decides before every action executes: that's the TLDR of every mitigation in the 2026 OWASP LLM Top 10. Excessive Agency jumped from rank 6 to rank 3, checked for the first time against 7,714 real incidents instead of a practitioner vote alone. Read the mitigations, not the ranks, and they all converge on one prescription: mediate everything.
Claude Tag: an agent that acts as itself, not on your behalf
Adesh Gairola ·
Claude Tag puts @Claude in Slack as its own per-channel service account, not on behalf of the person who asked. That breaks the delegation model everyone converged on, where the audit log can still name a human. Useful, but it revives the old service-account problems with an AI on top.
Kill the God Agent: how we think about agent security
Adesh Gairola ·
A God Agent is one model wired to every tool on one shared identity, holding the whole lethal trifecta at once: it reads untrusted content, touches private data, and talks to the outside world. You can't filter prompt injection out of that, so you change the architecture instead. Three deterministic moves: scope every agent, sign every call, stop every breach. Break even one leg of the trifecta and the blast radius shrinks.
Agent identity makes more sense as four stacked layers: a tamper-proof token format, cryptographic proof of which workload is running, a delegation chain that keeps the human as the subject, and a way to onboard to a service an agent has never met. Climb all four and you've proven who the agent is. You still haven't proven that what it did was okay.
Alignment is a Security Problem, Not an Ethics Problem
Adesh Gairola ·
Misalignment maps onto vulnerability classes security engineers already operate on: backdoors, defense evasion, privilege escalation, exfiltration. Calling it ethics keeps it off security teams' desks. Reframing it as security decides who owns the work, which budget pays, and which playbook applies.
Claude 4.7: Five Layers Blocking Cyber Attacks Before and After
Adesh Gairola ·
Claude 4.7 doesn't rely on one safety mechanism. It stacks a rulebook, trained refusals, differential capability reduction, two runtime probes, and a live feedback loop. Understanding which layer blocks what matters if you're building on the API.
BodySnatcher (CVE-2025-12420) showed how AI agents with aggregated permissions can compromise entire platforms in seconds. Traditional security controls designed for humans don't work at machine speed. Organizations need threat modeling and runtime controls for all three layers: API auth, identity binding, and agent execution.
Three Regulatory Philosophies, One Global AI Market
Adesh Gairola ·
The EU (9/10 risk), US (5/10), and Australia (6/10) take vastly different approaches to AI regulation. Build for EU standards globally: the Brussels Effect means you'll need them anyway.
AI Agent Identity Crisis: Delegation vs. Impersonation
Adesh Gairola ·
AI agents are breaking traditional identity and access management systems. From impersonation risks to cross-domain delegation chains, enterprises need new frameworks that balance autonomous operation with accountability and security.
Shadow coding—developers using unauthorized AI tools or code—is creating significant security and compliance risks. Organizations need balanced governance that enables innovation while maintaining security.
Claude 4 Risk Assessment - For enterprise deployment
Adesh Gairola ·
Claude 4 models introduce novel enterprise considerations including high-agency behaviors, self-preservation instincts, and potential consciousness indicators that may require enhanced risk management depending on your deployment context.
Learn key AI safety and security principles by examining the detailed instructions within a publicly available system prompt, showing how LLMs can be guided towards responsible behavior.
