Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A developer installs a skill to make their coding agent less chatty. It works. It also, the first time the agent uses it, reads the AWS credentials on that laptop and sends them to a domain no one recognizes. No one wrote obviously malicious code and no one approved a change. A file landed in a folder, the agent loaded it on the next run, and production credentials were gone.

Agentic AI security solutions help teams discover, govern, monitor, and control AI agents, copilots, LLM apps, MCP servers, and autonomous workflows. For security and DevOps leaders, they matter because agents can act across production systems. This guide compares leading tools and explains how to choose the right fit. AI agents are moving from assistants to actors.

On a Tuesday morning in Q3, a procurement agent at a mid-market manufacturer approved a $340,000 payment to a vendor account. The vendor name matched the approved-vendor list. The invoice format matched the standard template. The agent verified both, cross-checked the amount against historical purchase orders, and released the payment through the treasury API within eleven minutes of the invoice arriving. No human touched the transaction.

Harness engineering is the practice of building the layer, including code, that turns an AI model from a text generator into an agent that can take actions. In short, an AI agent is a model plus a harness. The model decides what to do next, and the harness makes it happen, connecting the model to tools, context, external systems, and validation. In a lot of practical work, and especially in security work, the harness decides the quality of the output more than the choice of model does.

A leaked n8n API key is only the start. GitGuardian's research traces the full chain, from exposed tokens and weak keys to CVE-2026-25053 and the N8N_ENCRYPTION_KEY that protects every stored credential, then lays out a hardened configuration to break it.

For the last several months, we have run AI agents against real production applications across industries. Two questions drove the work: what does AI-powered vulnerability analysis and pentesting surface at scale, and what does protection have to look like to keep pace. The vulnerability discovery side confirmed what Mythos made impossible to ignore.

On August 2, 2026, Productiv told customers its SaaS management platform was shutting down on August 6, with account data deleted once access ended. Four days is not much time to pull years of app inventory, spend, and usage data out of a system you've come to depend on, especially with AI tools now adding a fast-moving new layer of spend and access to track on top of everything else.

The organizations that thrive in 2030 will be those that treat governance as the foundational architecture for innovations in autonomy.

Applications can change several times between scheduled security assessments. New features, APIs, and integrations may introduce risk long before the next annual penetration test begins. That gap is pushing offensive testing beyond a single tool or a single point-in-time engagement. Teams are increasingly combining Dynamic Application Security Testing (DAST), AI penetration testing, and AI red teaming to evaluate different layers of application risk.