NEW! Data443 Acquires Vaikora – Real-Time AI Runtime Control & Enforcement for AI Agent
AI agent security is the discipline of securing autonomous AI systems that take actions on behalf of users or other systems. It covers identity and access management for AI principals, runtime enforcement of policy on agent actions, audit trails that survive assessor review, and protection against attack classes specific to language models such as prompt injection and tool misuse.
Most enterprises now run AI agents that touch production systems. The OWASP Top 10 for Agentic Applications was published in early 2026 to organize the risk surface, with prompt injection ranked first. Insurance underwriters and compliance auditors began asking specific questions about agent governance in the second half of 2025, and most security teams found their existing controls were designed for human users or for batch systems, not for agents that act on their own initiative.
The discipline has four pillars: identity for agents (who is calling, on whose behalf), action enforcement (what the agent is allowed to do under which conditions), audit (what the agent did and why each decision was made), and attack defense (preventing prompt injection, jailbreak, and tool misuse).
AI agent security is the broader category that contains AI runtime control, AI observability, and AI red teaming. It is distinct from traditional application security because the system being secured can reinterpret its own instructions in response to adversarial input.
A finance team deploys an AI agent that reconciles vendor invoices. AI agent security policies define which vendors the agent can pay, the maximum invoice amount it can process without human review, and the data classes the agent is allowed to read. A second example: an AI security operations agent that triages alerts is restricted from closing alerts above a severity threshold, preventing an over-eager agent from clearing real incidents.
LLM security focuses on the model layer (prompt safety, output filtering, content moderation). AI agent security focuses on the action layer (what the agent is allowed to do, with which permissions, with what audit trail). They are complementary controls in the same stack.
Agents need machine identities that can be granted scoped permissions. AI agent security treats the agent as a first-class principal in your identity system, applies least-privilege rules to it, and revokes its access when a session ends. IAM platforms are evolving to support this; AI agent security extends IAM with runtime policy.
Prompt injection (direct and indirect), jailbreak, tool misuse, data exfiltration through agent outputs, RAG poisoning, and unauthorized model access. The OWASP Top 10 for Agentic Applications catalogs the most common risks.
Typically a joint ownership between the security team (policy authorship, audit), the AI platform team (deployment, observability), and the compliance team (assessor evidence). Vaikora is designed to give each team the surface area it needs without forcing one team to own the whole stack.
Last updated: 2026-05-20.
