AI Security for Production Agentic Systems
Who controls this AI agent?
SecOps: We do. Here's the proof.
Agent identity, payload integrity, and trust enforcement—cryptographically verified on every request.
When auditors ask which agents accessed production, you don't correlate logs. You show proof.
Something Is Calling Your APIs Right Now
You can't prove which agent sent it. You can't prove the payload wasn't modified. That's the default.
Most failures aren't nation-state attacks. They're internal: a forked agent, a staging key that leaked to prod, a contractor's tool calling endpoints it shouldn't.
No Agent Identity
API keys don't identify agents. When an LLM calls your tool, you have a token. You don't have proof. Your SOC 2 auditor will ask how you verify agent identity. What's your answer?
No Payload Integrity
Payloads get modified across hops, proxies, retries, and queues. Without cryptographic binding, that "verified" request could have been tampered with three services ago.
No Trust Levels
Your dev agent and your production agent look identical to your tools. One is a self-signed test build. One has verified provenance. You treat them the same.
The Threat Is Already Inside
You don't need an APT. You need one engineer who deployed from a fork, one staging key that got committed, one contractor agent that "just needs read access." That's your threat model.
Agent Sprawl Is Already Here
Agents created by templates, copilots, CI jobs. Identity and provenance drift faster than your governance docs update. When the auditor asks "which agents can access production tools?"—can you answer?
Aligned with OWASP Agentic Security
We publish a detailed threat model, OWASP mapping, and hard boundaries. See the full matrix and guarantees on the Security page.
Evidence-First Security
We document exactly what we protect and what we don't. No vague claims.
AI Security Use Cases
CapiscIO fits wherever agents communicate. These are the patterns we see most.
Multi-Agent Orchestration
Agent A calls Agent B. You need to know it's actually Agent A. Not a fork. Not a replay. Not a test build that leaked to prod.
MCP Tool Server Protection
Claude is calling your database tool. Or is it? Know which LLM or agent is making the call before you expose DELETE access.
Agentic Workflow Security
Requests hop across services, queues, and retries. One decorator per endpoint. No protocol changes. Auth that survives the journey.
Audit-Ready Access Control
When the auditor asks "which agents touched production last quarter?"—you have cryptographic proof, not log correlation.
What CapiscIO Adds at Runtime
Three outcomes. Every request. When Guard is deployed.
Authenticated Caller
Know which agent sent this request. Cryptographic proof, not just a header.
Tamper Detection
Know if the payload was modified. Body hash binding catches any change.
Trust Threshold
Enforce minimum trust levels in your middleware. For example: require Level 2+ for production tools and deny self-signed dev callers.
Designed for Real Production Workflows
CapiscIO is purpose-built for agentic AI systems where requests hop across services and tool servers. If you're securing internal agent-to-tool calls, you need authentication and integrity that survives retries, queues, and multi-hop orchestration.
AI Security Products
Two protocols, two guards, one security model.
Agent Guard
For agent-to-agent communication. Caller identity, payload integrity, trust levels—verified before your handler runs.
- One decorator. No protocol changes.
- Python SDK or Go sidecar
- Sub-10ms verification overhead
MCP Guard
For MCP tool servers. Know which LLM or agent is calling before you expose write access to your database.
- @require_trust(level=2) on any tool
- Works with Claude, GPT, any MCP client
- Per-tool evidence logging (RFC-006)
Stop Trusting Anonymous Agents
CLI is free. SDK is open source. Production security starts in five minutes.
Or keep hoping nothing calls your production API that shouldn't.
AI Security FAQ
Practical answers for securing internal agent deployments