// the ai governance stack
Whether you're the CIO being asked to ship AI safely, the CTO who needs security sign-off, or the CISO evaluating what the governance gap actually is — the question is the same: "do I need this if I already have X?" The answer is yes — and here's exactly why.
// runtime enforcement layer
Every AI agent tool call passes through multiple layers. Most organizations have the top and bottom covered. The middle is what's blocking safe, broad AI deployment.
// the agent runtime layer is called out by name in
// architectural comparison
| Approach | Governs agent actions | Independent from agent | Three-state decisions | Behavioral context |
|---|---|---|---|---|
| RBAC / IAM | No | Partial | No | No |
| API Gateway | No | Yes | No | No |
| AI Guardrails | Partial | No | No | No |
| Observe-and-Detect | Post-hoc | Yes | No | Partial |
| Embedded Library | Opt-in only | No | Yes | No |
| Behavry (inline proxy) | Every tool call | Attestation separation | Allow / Deny / Intercept | Behavioral baselines |
// same agent, same permission, different context
Agent: data-analyst-primary Tool: database.query Time: 10:14 AM (business hours) Volume: Within baseline range BRF Score: 0.23 (low) Decision: ALLOW Full permissions. Normal pattern. Clear operational context.
Agent: data-analyst-primary Tool: database.query Time: 2:47 AM (off-hours) Volume: 340% above rolling baseline BRF Score: 0.81 (high) Decision: INTERCEPT → HITL escalation Same agent. Same permission. Same tool. Behavioral context changed the outcome.
// how behavry fits alongside what you already have
If you already have
These tools govern who has credentials and manage the secrets lifecycle. They are excellent at securing the front door — making sure agents authenticate with the right keys and that those keys are rotated, scoped, and inventoried.
They don't govern what an authenticated agent does once it's inside. They have no concept of whether a specific agent's tool calls are within its intended scope, no behavioral baseline to detect when an agent starts doing something it's never done before, and no pre-execution policy enforcement on individual actions.
Behavry doesn't replace your identity layer. It enforces behavioral policy at the MCP proxy layer, which sits above identity and below the agent's targets.
If you already have
IGA tools govern which humans have access to which systems — role provisioning, access certifications, entitlement lifecycle. They were designed for deterministic human users who log in, perform a task, and log out.
AI agents don't work that way. They reason, chain actions, shift scope mid-task, operate at machine speed, and can spawn sub-agents. The "access" granted to an agent is a starting point — what the agent does with that access is entirely outside what IGA was built to govern.
Behavry governs what autonomous agents do with their access at runtime — per tool call, per action, before execution. That's a fundamentally different problem than who is provisioned to access what.
If you already have
Observability and SIEM tools tell you what happened. They are indispensable for incident response, compliance reporting, and post-hoc correlation. If an agent exfiltrates data, your SIEM will eventually surface it.
"Eventually" is the problem. By the time a SIEM alert fires, the action has already executed. The data has already moved. The database record has already been deleted. Observability is retrospective by design — it's not built to intercept.
Behavry enforces policy before the action executes. Every tool call is evaluated against per-agent Rego policies at the proxy layer before it reaches the target server. Your SIEM still gets the audit trail — from Behavry, structured and attributed to a specific agent identity.
If you already have
Network security tools like Zscaler enforce zero trust at the network layer — ensuring traffic is encrypted, authenticated, and routed correctly. MCP gateways handle transport-layer routing and protocol. Both are excellent at what they do.
Neither has a concept of agent identity, behavioral baseline, per-agent RBAC, or a pre-execution policy engine that understands what a specific tool call means in the context of a specific agent's risk profile. Allowing an agent to call filesystem/read is a routing decision. Allowing this agent, in this risk tier, to read this class of file, in this session, is a governance decision.
Behavry works alongside your existing network and gateway stack — agents point at Behavry's proxy, which enforces policy and forwards through your existing infrastructure. Complementary layers, not competing ones.
If you already have
AI security tools in this category primarily govern by observation and attribution — they analyze what agents did, correlate behavior across sessions, and surface anomalies after the fact. Some offer LLM-level guardrails on model inputs and outputs. These are real capabilities. They are not enforcement.
The structural difference is architectural position. To enforce pre-execution policy, detect inbound injection before it reaches agent context, produce a cryptographically verified Decision Trace, or block a blast-radius violation in real time — you must be inline on the execution path. Observation tools are, by design, off the critical path.
The attestation separation principle makes this concrete: any entity that can act cannot independently attest to its own behavior. An agent cannot audit itself. A tool downstream of the execution path can only see what the agent already decided to emit. Independent inline governance is not a nice-to-have — it is a logical requirement.
Portal26 tells you what happened. Behavry stops it before it completes. JetStream gives you a governance dashboard. Behavry gives you a control plane — removing it doesn't reduce visibility. It breaks agent access entirely.
Whichever AI platform you choose
Governance in place on day one means security sign-off isn't the bottleneck to shipping AI. Configure policy once — every new agent, model, or framework your team adopts is governed automatically from the moment it registers. No per-deployment review cycle.
Behavry enforces at the tool call layer — the one place all agentic AI systems share regardless of model or framework. Blast radius limits, DLP blocking, injection detection, behavioral baselining. The protection travels with your agents no matter what they're built on.
Behavry doesn't care which model you use. Switch from GPT-4o to Claude to Gemini. Move from LangChain to CrewAI. Add an open-source model. The governance layer stays in place — your policy, audit trail, and risk scoring travel with every transition, not against it.
// behavry is additive, not disruptive
// the fundamental question
Every tool faces the same question from the CTO and the CFO: is this a nice-to-have or a requirement? The answer depends entirely on how it's positioned — and how it's deployed.
Behavry is designed to be mandatory by architecture — not by policy. The MCP proxy sits inline between every agent and every tool it touches. Removing it doesn't degrade governance. It breaks agent access entirely. That's not a feature. That's a control plane.
// early access
The platform is built and running. We're opening access to a limited number of organizations deploying AI now who need governance in place before they scale. Our founder works with you directly to get it deployed.