Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI application security protects AI-powered apps, including those powered by large language models ( LLMs), from unique threats like prompt injection, data poisoning, and model theft. It achieves this by securing the entire lifecycle, including code, data, algorithms, and APIs, using specialized tools and processes that go beyond traditional security measures. It involves securing the AI model’s behavior, training data, and outputs.

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.

We release some good stuff this week with the CertKit agent version 1.8 from our roadmap, along with some small usability fixes in the CertKit web application.

Agentic AI workloads are shipping to production on Kubernetes faster than the standards to secure them. Many teams deploying autonomous, tool-calling agents as containerized microservices do so without a shared baseline for securing or monitoring those containers. The CNCF AI Technical Community Group recently published a comprehensive article on cloud-native agentic standards, marking the first attempt to define best practices for such deployments.

On March 30-31, 2026, threat actors published two malicious versions of the popular HTTP library axios (versions 1.14.1 and 0.30.4) to the npm registry. Both versions included a new dependency named plain-crypto-js which, in its 4.2.1 release, contained a fully-featured cross-platform dropper that silently installed a Remote Access Trojan (RAT) on developer machines.

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.