AI Agent Security

Secure the action path before the agent acts.

AI agent security is the discipline of making autonomous authority visible, constraining sensitive actions, and preserving enough evidence for engineering, security, governance, and operations teams to trust what happened.

An agent's connected identity, tool, and action surfaces are separated by explicit control boundaries. — Secure more than the response.

Definition

What makes AI agent security different?

A chatbot can produce a risky answer. An agent can use context, assume an identity, call a tool, update a record, open a ticket, influence an approval, or trigger a business process. The security boundary therefore moves from model output to the complete path from input to business effect.

The practical goal is bounded autonomy. The agent may complete useful work, but its data access, tool use, action authority, approval path, and evidence trail remain explicit and testable.

Security Model

Six areas define the agent risk boundary.

These areas help teams move beyond generic AI concern and toward reviewable technical decisions.

Authority

What can the agent read, decide, approve, or change inside the workflow?

Tools

Which APIs, plugins, automations, and business systems can the agent invoke?

Context

Which documents, messages, memory, and retrieved sources can influence the agent?

Identity

Which human or service identity is used when the agent acts?

Controls

Which boundaries are enforced outside the model and close to the business action?

Evidence

Can reviewers reconstruct intent, decision, action, ownership, and control result?

Focused Guides

Separate pages for agent security workstreams.

Each guide has its own URL so teams can move directly from a search query to the relevant control, test, and evidence questions.

Agent Security Testing

Validate agent workflows with realistic adversarial inputs, tool states, identities, approvals, retrieval paths, and business effects.

Explore Agent Security Testing

Agent Permissions

Map and limit what agents can read, decide, approve, execute, and retain across enterprise workflows.

Explore Agent Permissions

Agent Tool Security

Secure agent tool use with operation-level permissions, parameter validation, destination controls, approvals, and traceable evidence.

Explore Agent Tool Security

Agent Red Teaming

Red team autonomous AI workflows from instruction and context through identity, tool use, approval, and evidence.

Explore Agent Red Teaming

Controls

Put protection close to the business action.

Instructions and guardrails can influence model behavior, but they should not be treated as the only authorization layer. Sensitive actions need deterministic enforcement: operation-level permissions, parameter validation, destination restrictions, data classification, approval gates, rate limits, transaction limits, and safe failure behavior.

Testing should confirm that these controls continue to work when prompts, retrieved context, tool responses, or user requests are adversarial, incomplete, stale, or misleading.

AI Agent Security FAQ

Questions teams ask before production.

What is AI agent security?

AI agent security is the practice of protecting autonomous AI workflows that can read context, make decisions, use tools, trigger business actions, and create operational evidence.

How is AI agent security different from LLM security?

LLM security focuses heavily on model input and output. AI agent security also covers identity, permissions, tool calls, approval paths, memory, retrieval, runtime behavior, and the business effect of agent actions.

What should enterprises test before deploying an AI agent?

Enterprises should test prompt injection, unsafe tool use, authorization boundaries, approval bypass, retrieval exposure, memory behavior, audit evidence, and safe failure under realistic workflow conditions.

Why are tool permissions important for AI agents?

Tool permissions define what an agent can actually do. If permissions are too broad, a model error, malicious instruction, or compromised context can turn into a real business action.

What evidence should an AI agent workflow preserve?

A reviewable workflow should preserve user intent, system context, retrieved sources, tool requests, tool responses, approvals, policy checks, final actions, and the owner responsible for each control.

How does Orbyntis evaluate AI agent security?

Orbyntis evaluates agent security by mapping the agent's authority boundary, testing controls against realistic abuse cases, and documenting evidence that security, governance, and operations teams can review.

Reference Alignment

Use public frameworks without flattening the workflow.

OWASP GenAI, MITRE ATLAS, and NIST AI RMF are useful inputs. Enterprise implementation still needs workflow-specific authority mapping, control validation, and evidence ownership.

OWASP GenAI / LLM Top 10 MITRE ATLAS NIST AI RMF