Runtime AI Security for Autonomous Systems
Secure and control AI agents, applications, workflows, models, tools, APIs, and data across the enterprise.
Aptori AIDR provides the runtime control layer between AI systems and enterprise resources, enabling teams to govern what AI can access, invoke, change, and do.
AI is spreading faster than governance.
Agents, copilots, assistants, and model-driven workflows are now acting across enterprise systems. The risk is not just what they say. It is what they can access, invoke, and change.
The enterprise question is no longer “can we use AI?”
It is “what is this AI allowed to do right now?”
Workflows execute
AI agents operate across tools, APIs, data, tickets, repositories, and business systems.
Context travels
Prompts, outputs, memory, files, and payloads carry sensitive enterprise context.
Reach expands
MCP servers, SaaS apps, APIs, and automations increase the AI execution surface.
Evidence is required
Teams need traceability across every request, decision, response, and action.
Existing enterprise controls were not built for AI agents.
Traditional security architectures were designed around deterministic software and human-driven workflows. AI agents change those assumptions by creating runtime execution paths across tools, APIs, data, and enterprise workflows.
Static permissions
Traditional IAM can identify who is calling, but not always whether an autonomous AI action should happen right now.
Deterministic traffic
Gateways were built for predictable API traffic, not AI-generated workflows that change at runtime.
Content inspection
DLP can inspect data movement, but AI risk also depends on tool use, intent, role, workflow, and context.
Prompt filtering
Basic guardrails inspect prompts and outputs, but cannot fully govern runtime behavior, action authorization, or tool invocation.
Embedded controls
Governance cannot be configured one assistant, workflow, or AI application at a time.
Fragmented evidence
Security, IT, and compliance teams need one view of AI decisions, runtime actions, and enforcement outcomes.
A control plane for enterprise AI.
Aptori AIDR sits between AI agents and enterprise systems, enforcing policy across applications, workflows, models, tools, prompts, outputs, data, and runtime actions.
Identify, authorize, inspect, enforce, and audit every AI interaction.
AIDR answers the operational question for every AI interaction: should this request, tool action, response, or data movement be allowed right now?
Identify
Understand the user, agent, app, role, team, workflow, and environment.
Authorize
Check what the agent, workflow, or application is allowed to access and do.
Inspect
Analyze prompts, responses, tool calls, payloads, memory, and data movement.
Enforce
Allow, block, redact, rewrite, throttle, reroute, or require additional approval.
Audit
Record every decision, prompt, response, tool invocation, and runtime action.
Runtime governance for AI actions.
AIDR combines enterprise AI gateway controls with detection, response, adversarial testing, runtime validation, and auditability for agents, applications, and AI-driven workflows.
Identity-aware AI security
Evaluate each AI request in the context of the agent, application, user, role, workflow, and environment.
Authorization for AI actions
Control what AI can access, which tools it can use, and which actions it can perform.
Prompt and output protection
Inspect AI inputs and outputs before unsafe instructions or sensitive data cause damage.
Agent and workflow control
Secure agentic workflows where tools, memory, permissions, and actions are chained together.
Runtime validation
Validate how AI systems behave when prompts, tools, APIs, data, and workflows interact.
Observability and audit
Give security, IT, and compliance teams visibility into every AI interaction and decision.
AI security is no longer about text.
AI systems no longer operate as passive assistants. They invoke tools, trigger workflows, access APIs, retrieve sensitive data, and orchestrate runtime actions across enterprise systems.
The security problem is no longer just generated content.
It is autonomous runtime behavior.
AIDR is not passive monitoring.
It acts inline before unsafe or unauthorized activity reaches a model, tool, user, API, or business system.
Approved requests continue to approved models, tools, and enterprise resources.
Unsafe activity is stopped before prompt attacks, unauthorized tool use, or policy violations create risk.
Risky prompts, responses, or payloads are redacted, transformed, sanitized, or corrected.
Traffic is routed based on sensitivity, cost, model approval, data classification, or policy.
Runtime threats in agentic AI systems.
As AI agents gain the ability to invoke tools, interact with APIs, and orchestrate workflows, the runtime attack surface expands. AIDR continuously validates and governs runtime AI behavior across modern agentic systems.
Continuously test how AI systems fail.
Simulate prompt injection, unsafe tool invocation, privilege escalation, workflow hijacking, and sensitive data exposure before attackers exploit them.
Why enterprises need AIDR.
Governance cannot be configured one bot, workflow, or application at a time. Enterprises need a central layer that makes AI adoption safe, observable, and manageable.
Safe AI agent rollout
Deploy agents, copilots, and AI workflows with policy enforcement built in.
Reduced leakage risk
Control sensitive data movement through prompts, outputs, tools, APIs, and workflows.
Central governance
Manage policy once and apply it across agents, workflows, models, and systems.
Audit-ready visibility
Trace every AI interaction, policy decision, model request, and tool action.
Runtime control
Enforce decisions inline before unsafe activity reaches enterprise systems.
Continuous validation
Test how AI systems fail and close unsafe runtime paths before they are exploited.
Deploy runtime AI governance where your enterprise requires it.
AIDR is designed for enterprise AI adoption across SaaS, dedicated, self-hosted, and restricted deployment models.
Fast deployment for centralized AI security, visibility, policy enforcement, and auditability.
Isolated deployment for enterprises that require stronger operational and data separation.
Operate AIDR within your own infrastructure, security boundary, and enterprise environment.
Support regulated environments where AI workflows and security controls must remain private.
Explore runtime AI security.
AI security extends beyond model responses. Modern AI systems invoke tools, access APIs, make decisions, and operate autonomously. Explore how Aptori AIDR provides runtime visibility, governance, and protection across the entire AI ecosystem.
Runtime security and governance for autonomous AI agents, workflows, and execution paths.
Explore agentic AI security → MCP SecurityRuntime governance for AI tool invocation, Model Context Protocol, and AI-to-API execution.
Explore MCP security → AI Runtime SecurityContinuous runtime validation, enforcement, and authorization for enterprise AI systems.
Explore AI runtime security →Clear answers for enterprise AI security teams.
For teams planning AI agent, copilot, workflow, and enterprise AI application deployments.
What is Aptori AIDR?
Aptori AIDR is an enterprise AI control layer for securing and governing AI agents, applications, workflows, models, tools, prompts, outputs, data access, and runtime actions.
What is runtime AI security?
Runtime AI security continuously validates and governs AI behavior during execution, including tool invocation, API access, workflow orchestration, and autonomous actions.
How does AIDR help with enterprise AI agents?
AIDR makes enterprise AI agents safer to deploy by enforcing identity-aware access, tool permissions, data handling policies, model routing, monitoring, and audit trails.
How is this different from basic AI guardrails?
Basic guardrails usually inspect prompts or outputs in isolation. AIDR adds enterprise context: who the user is, what they are allowed to access, which tools are being invoked, how data moves, and what actions should be enforced in real time.
Does AIDR only secure models?
No. AIDR secures the broader AI operating environment: agents, assistants, models, tools, APIs, workflows, memory, outputs, and runtime behavior.
What is AI action governance?
AI action governance controls what actions AI systems are authorized to perform across enterprise systems, APIs, workflows, tools, and data environments.
What is MCP security?
MCP security governs Model Context Protocol integrations, tool invocation, and runtime interactions between AI systems and enterprise services.
How do AI agents create security risk?
AI agents create new runtime attack surfaces by invoking tools, accessing APIs, orchestrating workflows, and making autonomous runtime decisions.
Deploy AI agents while maintaining control over identity, access, data, policy, tools, models, and runtime behavior.
Move beyond prompt filtering and fragmented governance. Control AI actions with runtime validation, inline enforcement, and audit-ready evidence.
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