As AI agents exploit APIs at machine speed, AppSentinels prevents unauthorized and unintended actions by enforcing business logic and operational intent.
The Instagram account takeover incident highlighted a new reality: AI agents with privileged access can be manipulated, making business logic enforcement essential for securing autonomous systems.
AI agents with privileged access need business logic guardrails to prevent authorized abuse.
AppSentinels covers the full agentic attack surface through four continuous capabilities: the same pillars that secure your APIs, now extended to the agents that drive them.
Inventory every agent, MCP server, and tool, including shadow agents spun up by developers and AI assets, custom runtimes, and SaaS platforms.
Continuously probe your agents and the APIs they call, looking for prompt injection paths, tool poisoning, missing authorization, privilege escalation chains, and intent violations that emerge only at the logic layer.
Enforce ownership, intent, and sequence on every agent action in real time. When an agent’s chain of calls deviates from legitimate workflows, even if every individual request is authorized, we block at the logic layer.
AI agents don't live at the network perimeter. They run inside your Kubernetes clusters, behind your microservice mesh, inside your cloud VPCs. AppSentinels deploys with them and not in front of them.
See AppSentinels discover your AI attack surface, stress-test your agent workflows, and enforce business logic, live in your environment.
Prompt hardening makes an AI agent’s instructions resilient to injection and manipulation. AppSentinels’ Red-Teamer attacks your prompts using direct injection, indirect injection via documents or tool outputs, multi-turn manipulation, and jailbreak patterns. Where it finds gaps, it generates specific fixes: instruction boundaries, explicit refusal rules, tool output validation, and contextual action constraints.
It learns before it enforces. AppSentinels observes normal traffic to build a baseline, covering expected API sequences, session behavior, data access patterns, role-specific actions. Enforcement rules come from that observed baseline, not hand-written policies, so legitimate workflows are already included. When a deviation is flagged, teams can review and approve edge cases, which fold back into the model. False positive rates drop sharply within the first few weeks.
