Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Every guide to AI agent observability tells you what to capture — prompts, tool calls, token usage, traces, syscalls. Almost none address which of those signal sources you can still trust when the agent itself is part of the threat model. That distinction is the entire difference between observability that helps your SRE team debug a slow reasoning chain and observability that helps your security team investigate a breach.

When AI agent workloads start generating more alerts than your SOC can keep up with, the instinct most teams reach for is to deploy more triage on top of what they already have. If the SIEM is producing thousands of atomized alerts, plug in something downstream that can cluster, prioritize, and auto-resolve them faster than a human can. The market has consolidated around exactly this answer.

At 2:47 PM on a Tuesday, a customer support agent receives a routine ticket asking about return policy edge cases. The agent retrieves a section from your internal policy wiki through RAG to formulate the response. Three weeks earlier, an attacker had planted a hidden instruction in that wiki page. Bedrock Guardrails scored the retrieved context at 0.04 — well within benign range.

MITRE ATLAS catalogs sixteen tactics and eighty-four techniques adversaries use against AI systems, including fourteen agent-focused techniques added through the October 2025 Zenity Labs collaboration. It is the canonical taxonomy a security architect’s CISO, auditor, or RFP will name. It is not a detection plan. ATLAS organizes around adversary objectives.

It usually starts the same way. The CISO comes back from a board meeting having signed off on agentic AI for production. The SOC lead is told, in roughly that many words, to build detection for the agents. And the security stack she has — CNAPP for posture, EDR on the nodes, container runtime sensors, a SIEM ingesting everything — was architected before AI agents existed as a workload class.

A platform team finishes a two-week observation window on a new internal research agent. The baseline is stable; the sensor produced a clean profile. By Friday, no policy has shipped — and the blocker isn’t tooling.

The residency evidence GDPR and the EU AI Act now expect lives in the runtime trajectory of every AI agent execution, not in the deployment configuration. Your residency compliance dashboard — every workload in eu-west-3, sovereign cloud configured, SCCs signed — cannot produce it. Your AI agent’s last thousand inferences crossed an external border, on average, eight times each. The translation API routed through us-east-1 when the EU endpoint hit capacity.

Editing IAM policies cannot fix the most common architectural mistake in shipping AI agents on Kubernetes. It happens in thirty seconds: a platform engineer reuses an existing ServiceAccount with an IRSA annotation for Bedrock access because creating a new one takes thirty minutes plus a Terraform pull request. The new agent ships under the existing identity.

Your risk committee meets Thursday. The agenda has a new item: AI agent risk posture. You open the register. The fraud detection agent shipped in March is on it. So is the customer service agent. Neither row is useful — “likelihood: medium, impact: high, control: service account scoped via IAM.” Three months ago that was approximately right. Last week the platform team added two MCP connections, the model was upgraded, and the agent now touches data classes the entry never anticipated.

Most “hardening” advice for AI agents is a checklist of things to configure before the agent runs. CIS Kubernetes Benchmark gates. Pod Security Standards baselines. NetworkPolicy templates. None of it’s wrong — it’s just one of four phases, the one your stack already covers. The other three are Observe, Enforce, and Reconcile. They’re where AI agents actually get breached, and they’re where most stacks have nothing.

Every AI workload security PoC reaches the same conversation. Platform engineering pushes back: the AI team won’t accept extra latency on inference. The security engineer hunts for benchmarks and finds a contradiction. Langfuse publishes 15% overhead. AgentOps publishes 12%. The security vendor quotes 1–2.5%. None is lying. They measure different layers.

It’s 2 a.m. and the SOC has a Tier 3 page. A customer-service agent on the production cluster has just wired refund payments to seven addresses outside the approved disbursement list. The runbook is unambiguous: isolate the pod, image the disk, image the memory, root-cause within 48 hours.

Your AI agent runs on AKS with a managed identity that can read Azure Key Vault, and you assume prompt injection is a theoretical risk—until a malicious prompt drives that agent to steal credentials from the Azure metadata endpoint in under a minute. Most teams discover this gap when their SIEM shows a single request to 169.254.169.254, but they cannot trace it back to which agent tool or prompt triggered it, or how far the stolen token traveled across their Azure environment.

For six weeks, a mid-size hospital system’s CDS agent issued recommendations biased by a poisoned guideline summary. No detection alert fired. The drift — denial recommendations in cases sharing one specific clinical attribute — traced back to a guideline an outside contributor had quietly reweighted in editorial review. Every existing detection stack reported green. DLP: no PHI left the cluster. EHR audit log: agent reading and writing within scope. Network egress: normal traffic.

A healthcare CISO opens her AI-SPM dashboard at the start of the quarter. Every clinical AI agent in the cluster reads green: full AI-BOM coverage, every permission scope reconciled, the HIPAA compliance tag clean across the fleet. The ambient scribe, the prior-authorization assistant, the oncology decision support agent — all monitored, all green, all the way through. Six months later, the Office for Civil Rights opens an investigation.