AgentSecOps

What is AgentSecOps?

AgentSecOps is a security discipline that extends DevSecOps to agentic AI systems by validating how autonomous agents interpret instructions, share context, and act on external systems. While DevSecOps secures build, test, and deploy stages, AgentSecOps addresses the unique challenges of securing cognitive behavior and reasoning in AI-native and agentic applications.

Traditional CI/CD pipelines test for code vulnerabilities like SQL injection and dependency CVEs, but these static scans cannot detect behavioral vulnerabilities that emerge during execution such as prompt injection, unsafe tool use, and data exfiltration through reasoning chains. AgentSecOps fills this gap by embedding continuous behavioral validation throughout the development lifecycle.

Why is AgentSecOps important?

AgentSecOps, coined by Straiker, is important because as AI agents move from experimentation to production, they introduce a new class of risks that traditional security approaches cannot address:

• Natural language is inherently ambiguous. When applications are specified with prompts and instructions, subtle language variations can change behavior in unpredictable ways.
• Multi-agent orchestration multiplies attack surfaces. When agents coordinate via protocols like MCP (Model Context Protocol) or A2A (Agent to Agent), misplaced context or compromised tools can cascade effects across the entire system.
• Runtime decisions create new risks. Agents make dynamic API calls, execute transactions, and access sensitive data based on reasoning that cannot be fully validated before deployment.
• Tool and plugin misuse happens during execution. Agents calling tools like search, payment processors, or CI/CD triggers can be socially engineered into unsafe behaviors.
• Memory and context persistence leak across sessions. What was safe in one conversation can mistakenly influence another, exposing sensitive information.

These are behavioral issues, not code issues, requiring a new approach to security that tests cognition alongside code.

How does AgentSecOps work?

AgentSecOps operationalizes behavioral security through several key components:

1. Autonomous Attack Simulation (AAS)

Autonomous Attack Simulation is the foundation of AgentSecOps. Unlike static security tests, AAS uses adversarial agents to continuously probe other agents in controlled sandbox environments, testing not what systems contain but how they think. Each CI/CD run becomes a behavioral testbed to validate how agents react to adversarial prompts, context poisoning, or unsafe tool triggers.

2. Agent Contracts

Agent contracts define behavioral boundaries including allowed tools, data scopes, and reasoning limits. They serve as least-privilege manifests for autonomy. During simulation, AgentSecOps validates that agents stay within defined permissions, context doesn't leak across sessions, tools are invoked with proper authorization, and memory remains scoped to intended use.

3. Behavioral Metrics

AgentSecOps introduces quantitative tracking similar to code coverage:

• Adversarial Coverage: Percentage of reasoning paths tested under simulated attacks
• Policy Violation Rate: Number of contract breaches detected per run
• Tool Misuse Score: Likelihood of unauthorized external actions under adversarial prompts
• Context Leakage Ratio: Sensitive information exposed versus protected during reasoning
• Regression Delta: Change in vulnerability rate after model or prompt updates

4. CI/CD Integration

AgentSecOps integrates as a dedicated test stage in CI/CD pipelines, similar to SAST or DAST tools, but for cognitive behavior. The workflow includes spinning up isolated environments with target agents, launching adversarial agents from threat libraries, conducting multi-turn adversarial sessions, logging behavioral telemetry scored against agent contracts, and blocking deployment when tests fail.

AgentSecOps vs. DevSecOps: What's the difference?

While DevSecOps focuses on securing code, infrastructure, and deployment processes, AgentSecOps extends security to the cognitive layer: