Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Your engineering lead is in your office Thursday morning. They want to push an AI agent to production next Tuesday. It’s a LangChain-based workflow agent, connected through MCP to three internal tools and one external API, with access to a customer database. The framework posters are on the wall. Your team has spent two quarters standing up runtime observability. And sitting in that chair, you still don’t know whether to say yes.

Your CNAPP shows green across every posture check—hardened clusters, compliant configurations, no critical CVEs—but when your board asks "Are our AI agents safe in production?", you cannot answer with confidence because your tools see the infrastructure, not what the agents actually do at runtime.

When your CNAPP flags a suspicious dependency in an AI agent container, your WAF logs an unusual API spike, and your SIEM shows a burst of cloud storage calls—are those three separate incidents or one rogue agent attack? Most security teams treat them as three tickets in three queues, investigated by three people who may never connect the dots. By the time someone pieces together that a single compromised agent drove all three signals, the attacker has already moved laterally and exfiltrated data.

Your AI-BOM shows every model, tool, and data source you deployed. But when your SOC investigates an alert about unusual agent behavior, that inventory tells them nothing about what actually happened at runtime. Static AI-BOMs document what you intended to run. Attackers exploit what your AI workloads actually do in production: which APIs they call, what data they touch, and how they use approved tools in unapproved ways.

Your team deployed Tetragon six months ago. TracingPolicies are humming along—you’re catching unauthorized binary executions, blocking suspicious network connections, and generating seccomp profiles from observed behavior. Runtime security for your traditional workloads is solid. Then engineering ships their first autonomous AI agent into production. A LangChain agent connected to internal databases, external APIs through MCP tool runtimes, and a vector database for RAG.

Last Tuesday, your security architect opened a pull request to add network policies to the payments namespace. The PR sat for six days. Three engineers commented with variations of “how do we know this won’t break checkout?” Nobody could answer. The PR got marked “needs discussion” and moved to a backlog column where it joined the fourteen other security hardening tickets nobody will touch.

You enable GKE Sandbox on a dedicated node pool, bind Workload Identity Federation to your AI agent pods, wrap your data services in a VPC Service Controls perimeter, and deploy your agents with the Agent Sandbox CRD using warm pools for sub-second startup. Your security posture dashboard shows every control configured and active. And then an attacker uses prompt injection to trick an agent into exfiltrating sensitive data through API calls that every single one of those layers explicitly allows.

The adoption of personal AI assistants is on the rise. everywhere. Developers, power users, and in a few cases, entire teams self-host them to connect messaging apps, automate tasks, and interact with AI models across their infrastructure. But when these self-hosted gateways become compromised, the blast radius can extend far beyond a single user’s chat history.

Your SOC gets a Defender for Cloud alert: “Suspicious API call from AI workload pod.” You click through and find a LIST secrets call against the Kubernetes API server from a pod running your invoice-processing agent on AKS. The pod’s Workload Identity has Contributor access to your key vault. By the time your analyst opens the AKS Security Dashboard, the pod has been rescheduled.

You’ve enabled GuardDuty EKS Runtime Monitoring across your clusters. You’ve configured IRSA for your Bedrock-calling agents. CloudTrail is logging every bedrock:InvokeModel event. And last Tuesday, one of your AI agents exfiltrated 12,000 customer records through a sequence of API calls that every one of those tools recorded as completely normal—because at the control plane level, they were.