Introducing Agent Security

2026-03-23 · Source: Blog RSS Feed | Snyk · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Intermediate, medium

Summary

Snyk has introduced Agent Security, a new approach to securing the AI agent lifecycle from code to runtime, marked by the general availability of Evo AI-SPM. This foundational module creates a definitive system of record for AI risk, addressing the "Shadow AI" problem where organizations lack visibility into autonomous agents. AI risk often begins in development, with components introduced rapidly, creating blind spots. Snyk's solution secures agentic development through Agent Scan (Open Preview) for supply chain assessment, Snyk Studio for AI-generated code security (deployed across 300+ enterprise customers), and Agent Guard (Private Preview) for real-time behavior enforcement. For agentic applications, Agent Red Teaming (Open Preview) simulates attacks, and Snyk API & Web performs dynamic testing. The core security model integrates Visibility, Intelligence, and Enforcement. Evo AI-SPM continuously discovers AI components in code and workflows, enriching them with risk intelligence. Snyk data from 500+ Evo scans shows a single AI model deployment can introduce nearly three times more untracked software components.

Key takeaway

For MLOps Engineers or AI Security Engineers managing AI agent deployments, you must establish a robust security posture from development through runtime. Your organization faces significant "Shadow AI" risks if you lack visibility into agent components and behaviors. Implement solutions like Evo AI-SPM to continuously discover AI assets, apply risk intelligence, and enforce policies early in the development lifecycle. This proactive approach prevents unsafe configurations and behaviors from reaching production, ensuring controlled and secure AI agent adoption.

Key insights

Securing AI agents requires comprehensive visibility, intelligence, and enforcement across the entire development and runtime lifecycle.

Principles

• AI risk starts in development.
• Visibility precedes governance and control.
• Connect visibility, intelligence, enforcement.

Method

Implement continuous discovery of AI components in code, enrich assets with risk intelligence, and enforce policies before production deployment.

In practice

• Use Agent Scan for supply chain assessment.
• Embed Snyk Studio for AI-generated code.
• Deploy Agent Guard for real-time behavior.

Topics

• AI Agent Security
• Evo AI-SPM
• Shadow AI
• Software Supply Chain Security
• Prompt Injection
• Real-time Policy Enforcement

Best for: CTO, VP of Engineering/Data, Executive, AI Security Engineer, MLOps Engineer, Director of AI/ML

Related on AIssential

• Building AI Security with Our Customers: 5 Lessons from Evo’s Design Partner Program — Effective AI security requires continuous visibility, tailored detection, and enforced policies for custom and agentic AI systems.
• Microsoft takes Agent 365 out of preview as shadow AI becomes an enterprise threat — Autonomous AI agents pose urgent operational and security risks requiring comprehensive enterprise governance.
• OpenClaw and Claude Opus 4.6: Where is AI agent security headed? — AI agents and rapid development necessitate stringent security, zero-trust principles, and comprehensive inventory management.
• Securing our codebase with autonomous agents — Autonomous agents can significantly scale codebase security by automating vulnerability identification and remediation.
• Five Eyes spook shops warn rapid rollouts of agentic AI are too risky — Agentic AI systems introduce significant security risks due to expanded attack surfaces and unpredictable behavior.
• You Patched LiteLLM, But Do You Know Your AI Blast Radius? — The LiteLLM compromise reveals AI system risk extends beyond dependencies to runtime access and hidden connections.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Blog RSS Feed | Snyk.