Ecosystem · Runtime authorization layer
Where Behavry sits in your 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 attestation gap actually is, the question is the same: "do I need this if I already have X?" The answer is yes, and here is exactly why.
The stack your agents run through.
Every AI agent tool call passes through multiple layers. Most organizations have the top and bottom covered. The middle is the layer that makes admissibility decisions, and it is missing.
| Layer | What it covers | Tools |
|---|---|---|
| Infrastructure · Cloud & Compute | AWS, Azure, GCP. The physical substrate. Covered by your cloud security posture tools. | Wiz · Orca · Prisma |
| Identity · Human & NHI | Who has credentials. Secrets lifecycle. Service account governance. The front door. | Okta · Astrix · CyberArk · Entro |
| Access governance · IGA / Provisioning | Which humans are allowed access to which systems. Role lifecycle. Access reviews. | SailPoint · Saviynt · Cakewalk |
| Agent runtime · Behavry, inline action authorization | What autonomous agents are allowed to do with their access, at the moment of action. Per-agent identity, OPA policy enforcement, input scanning, behavioral baselines, risk scoring, Decision Trace. This is the gap. | Behavry |
| Multi-agent · Behavry, workflow authorization | Workflow sessions, delegation token chains, causal depth limits, permission ceilings. Individually well-behaved agents cannot collectively exceed their combined scope. Decision Trace spans the full pipeline. | Behavry |
| Transport · MCP Gateway / API Proxy | Secures the connection. Handles routing and protocol. No agent identity, no behavioral awareness, no pre-execution authorization. | solo.io agentgateway · Kong · Apigee |
| Targets · MCP Target Servers | Filesystem, database, GitHub, Slack, APIs. The systems agents act on. | PostgreSQL · GitHub · Slack · S3 |
| Observability · SIEM | Records what happened. Correlates signals after the fact. Now receives structured, agent-attributed Decision Trace events from Behavry. | Splunk · CrowdStrike · Datadog · Elastic |
The agent runtime layer · This is the gap
The agent runtime layer is called out by name.
This isn't a category we invented. The industry's own frameworks describe the layer, and the gap, explicitly.
Agentic AI IAM Framework & MAESTRO. CSA's purpose-built framework describes exactly what Behavry implements: agent identity, Zero Trust policy enforcement, secure delegation, and real-time behavioral monitoring. MAESTRO identifies authorization hijacking and untraceability as the primary attack surfaces.
Industry-standard LLM vulnerability taxonomy. The three highest-priority risks, Prompt Injection (#1), Sensitive Data Disclosure (#2), and Excessive Agency (#6), all require inline enforcement at the agent-tool boundary. Behavry addresses all three directly.
Practices for Governing Agentic AI Systems. Constrain the action space, require human approval for high-impact actions, maintain audit trails, preserve the ability to interrupt. Behavry's Intercept escalation, OPA enforcement, and kill switch are direct implementations.
Every tool stops at a boundary. The harm lives in the crossing.
| Approach | Authorizes 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 SDK / Library | Opt-in only | No | Yes | No |
| Behavry (inline authorization) | Every tool call | Attestation separation | Allow / Deny / Intercept | Behavioral baselines |
Behavry doesn't ask for intent. It evaluates behavior.
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 → human approval Same agent. Same permission. Same tool. Behavioral context changed the outcome.
RBAC allows both. Logging records both after the fact.
Behavry sees the difference before the action executes. Behavioral baselines, temporal patterns, volume anomalies, and six dimensions of the Behavry Risk Framework.
The entity that acts cannot attest to its own behavior. An embedded SDK asks the agent to declare its intent. An inline authorizer evaluates it independently.
Six questions. Six direct answers.
01 · If you already have Okta, CyberArk, SGNL, or another IAM / NHI tool
Okta CyberArk Astrix Entro SGNL BeyondTrust
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 authorize 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, and no pre-execution policy enforcement on individual actions.
Behavry doesn't replace your identity layer. It enforces behavioral policy at the action layer, which sits above identity and below the agent's targets.
Your IAM secures the credential. Behavry authorizes the action.
02 · If you already have SailPoint, Saviynt, or another IGA tool
SailPoint Saviynt Omada One Identity
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 authorizes 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.
Your IGA governs which humans have access. Behavry authorizes what agents do with it.
03 · If you already have Splunk, CrowdStrike, Datadog, or a SIEM
Splunk CrowdStrike Datadog Elastic Sumo Logic
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.
Behavry authorizes before the action executes. Every tool call is evaluated against per-agent Rego policies before it reaches the target. Your SIEM still gets the audit trail, from Behavry, structured and attributed to a specific agent identity.
Your SIEM tells you what happened. Behavry decides whether it should.
04 · If you already have an MCP gateway, API proxy, or network security layer
Zscaler solo.io agentgateway Kong Apigee nginx
Network security tools 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. 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 an authorization decision.
Behavry works alongside your existing network and gateway stack. Agents point at Behavry's authorizer, which evaluates policy and forwards through your existing infrastructure. Complementary layers.
Your network layer secures the connection. Behavry authorizes the action.
05 · If you already have JetStream, Portal26, Singulr, Prompt Security, or another AI-specific security tool
JetStream Security Portal26 Singulr AI Prompt Security Lakera Guard Cisco AI Defense Wiz AI-SPM Palo AI Runtime Credo AI
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 authorization.
The structural difference is architectural position. To enforce pre-execution policy, detect inbound injection before it reaches agent context, produce a verifiable Decision Trace, or block a blast-radius violation in real time, you must be inline on the execution 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.
Portal26 tells you what happened. Behavry decides whether it should. JetStream gives you a dashboard. Behavry gives you a control plane: removing it doesn't reduce visibility, it breaks agent access entirely.
They observe and attribute. Behavry authorizes, before execution, not after.
06 · Whichever AI platform you choose: OpenAI, Claude, Gemini, open-source, or whatever comes next
OpenAI / GPT Claude Gemini Ollama LangChain CrewAI LangGraph AutoGen AWS Bedrock Azure AI Foundry
Authorization 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 authorized automatically from the moment it registers. No per-deployment review cycle.
Behavry enforces at the action layer, the one place all agentic systems share regardless of model or framework. Blast radius limits, input scanning, 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 to Claude to Gemini. Move from LangChain to CrewAI. Add an open-source model. The authorization layer stays in place: your policy, audit trail, and risk scoring travel with every transition.
The model is your choice. The authorization is constant
Everything you already have keeps working.
Behavry fills the gap between them.
Okta, CyberArk, SGNL, Entro. Credential governance and NHI management unchanged.
SailPoint, Saviynt. Human provisioning, role lifecycle, and access certifications unchanged.
Splunk, CrowdStrike, Datadog. And now they receive structured, agent-attributed Decision Trace events from Behavry. Enhanced.
Per-agent identity, pre-execution policy enforcement, behavioral baselines, risk scoring, inbound injection detection.
Delegation chains, workflow session tokens, Decision Trace, causal depth limits, spanning the full agent pipeline.
Four models, below. Same authorization layer in every one.
We run everything. Fastest deployment. Best for teams that want to ship AI now without infra overhead.
Data plane in your VPC. Agent traffic never leaves your network. Control plane managed by Behavry.
Full stack in your cloud account. Image lifecycle managed by us. For enterprise and regulated industries.
Everything on-premises. No external dependencies. Built for air-gapped, government, and financial environments.
Optional tools get cut. Infrastructure gets budget.
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.
- Agents run without it
- Cut when budgets tighten
- Nice dashboard, ignored in incidents
- Competes with every other AI security tool
- Agents cannot operate without it
- Required to ship AI broadly and safely
- Authorization happens before damage, not after
- Sits inline: removing it breaks agent access
Behavry is designed to be mandatory by architecture, not by policy. The authorizer sits inline between every agent and every tool it touches. Removing it doesn't degrade authorization. It breaks agent access entirely. That's not a feature. That's a control plane.
The platform is built and running. We're opening access to a limited number of organizations deploying AI now who need authorization in place before they scale. Back to overview.
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