Agentic AI

AI Security in 60 Seconds - How Do You Secure an AI That Can Take Action?

AI models operate inside a larger system of identities, data, tools, infrastructure and applications.

Kouribea Saeed

Kouribea Saeed

— 2 min read
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AI Security in 60 Seconds - How Do You Secure an AI That Can Take Action?
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Modern AI is no longer limited to answering questions. AI agents can read files, access databases, browse the web, write code, call APIs and execute actions. [1][2], That changes the security model.

Traditional security asks:

“Can this user access the system?”

AI security must also ask:

“What is the AI allowed to see, decide and do?”

A secure AI environment therefore needs several layers of protection:

  • Identity & Access: Authenticate users and enforce RBAC, scoped identities and least privilege. [2][3]
  • Prompt Security: Defend against prompt injection, malicious instructions and attempts to manipulate an agent's goals. [2][3]
  • Model Security: Govern approved models, system instructions, model outputs, integrity and grounding. [1][4]
  • Agent & Tool Security: Restrict which tools agents can call, minimize permissions and credentials, and require human approval for sensitive actions. [2][3]
  • Runtime & Network Security: Isolate risky execution, restrict network access and harden the infrastructure surrounding AI workloads. [4][5]
  • Data Security: Protect sensitive information, documents, RAG data, embeddings, memory, models and secrets. [1][2][5]
  • Monitoring & Governance: Log and monitor AI activity, tool usage and security events so actions can be traced, reviewed and investigated. [1][4][5]

The Key Principle

Never rely on the AI model alone to enforce security.

AI models operate inside a larger system of identities, data, tools, infrastructure and applications. Security therefore needs to be enforced around the model through permissions, isolation, validation, policy, monitoring and human oversight. [1][3][5]
💡
Secure AI = Secure Model + Secure Context + Secure Tools + Secure Runtime + Governance

References

[1] NIST Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1), 2024.

[2] OWASP GenAI LLM Top 10, 2026.
Covers major risks to LLM applications including prompt injection, sensitive-information exposure, poisoning and other GenAI security weaknesses.

[3] OWASP Top 10 for Agentic Applications, 2026.
Addresses agent-specific threats including agent goal hijacking, tool misuse, identity and privilege abuse, unexpected code execution, memory/context poisoning and rogue agents.

[4] MITRE ATLAS: Adversarial Threat Landscape for Artificial-Intelligence Systems.
A threat-informed knowledge base of adversarial tactics and techniques targeting AI-enabled systems.

[5] NSA, CISA, FBI and international partners Deploying AI Systems Securely: Best Practices for Deploying Secure and Resilient AI Systems, 2024.

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