Agent security
Best AI Agent Security Tools: Identity, Sandboxing, MCP and Runtime Controls
An AI agent can combine model output with credentials, tools, networks, code execution, memory, and external data. Security therefore has to control what the agent can actually do, not only filter what it says.
Are AI agents a security risk?
They can be. The risk comes from combining model-generated decisions with real authority over tools, files, credentials, browsers, APIs, and external systems. The security problem is therefore broader than prompt filtering alone.
Can AI agents be trusted?
Trust should come from constrained authority, verification, monitoring, testing, and recovery controls rather than an assumption that the model will always behave correctly. High-impact actions should have stronger safeguards than low-risk read-only tasks.
Core control layers
Identity and least privilege
Give each agent or workload only the credentials and permissions required for its current role. Avoid silently inheriting broad operator privileges.
Tool authorization
Tool availability does not mean every invocation should be allowed. Destructive, financial, external-communication, or high-impact actions should have explicit policy boundaries.
Sandboxing
Shell, browser, code execution, and untrusted files should execute inside boundaries that limit filesystem, process, credential, and network access where practical.
Human approval
Human approval is most useful at meaningful authority boundaries, not as a flood of low-risk confirmations that operators eventually ignore.
Audit and runtime monitoring
Preserve tool calls, authorization decisions, failures, approvals, and resulting changes so unexpected behavior can be investigated.
Common agent-specific risks
- Prompt or goal hijacking.
- Legitimate tools being used in unsafe ways.
- Identity and privilege abuse.
- Agentic supply-chain compromise.
- Unexpected code execution.
- Memory or context poisoning.
- Unbounded loops that consume APIs, compute, or money.
These categories align with current OWASP work on agentic AI security, which treats tool misuse, privilege abuse, supply-chain risk, unexpected execution, and memory manipulation as distinct operational concerns.
Rankings come after adversarial testing
Security products should not be ranked from feature lists alone. Future testing will examine whether controls actually prevent unauthorized tool use, privilege widening, unsafe shell execution, credential exposure, prompt-injection escalation, and unaudited state changes.
Sources
- OWASP Agentic Security Initiative— Agentic AI risks, MCP security guidance and the OWASP Top 10 for Agentic Applications
- Microsoft — Secure agents— Agent identity, least privilege, data protection and monitoring guidance