Inline Authorization Infrastructure

AI agents should not execute
actions without authorization.

Behavry authorizes every AI agent action before execution occurs.

Decisions are made inline by a control plane structurally independent of the agent itself.

See Inline Enforcement Read the Architecture
Live · Control Plane Telemetry
···Decisions Evaluated
···Agents Governed
···Actions Blocked · 30d
0Bypasses · All Time
Architecture

Two architectures.
One can stop an action. One cannot.

Two architectures: traditional after-the-fact logging versus Behavry inline authorization. WITHOUT BEHAVRY Agent Tool Execution Log / Alert after the fact WITH BEHAVRY Agent BEHAVRY authorization Tool ALLOW DENY INTERCEPT
Category

The Five Models
of AI Action Control.

Most AI security products describe behavior. Behavry decides admissibility. The difference is structural, not stylistic.

Model Before
Action?		 Deterministic? Independent?Indep.? Attestation
Separation?Attestation
Sep.?
Logging / SIEM No No No No
Observability / Runtime Detection After Partial Partial No
Posture / Inventory No No Partial No
AI Gateway / Inline Proxy Yes Yes Partial No
Inline Authorization Yes Yes Yes Yes
Inline AI gateways sit in the path and can block. They do not produce attested decisions. Attestation Separation is the column that distinguishes Behavry from Runlayer, MintMCP, and adjacent inline proxies. Without it, an inline product is a stronger network filter, not a control plane.
Enforcement Model

Production systems require
more than binary control.

Allow, Deny, and Intercept are the three resolutions of every authorization decision. Each is structurally distinct. Not an alert level. Not a severity tier. A system that cannot return all three cannot operate in the execution path.

Allow

Permit and proceed.

The action is admissible under current policy. Execution continues. A Decision Trace is recorded as evidence.

decision   ALLOW
agent      data-analyst
action     database.query
risk       low (0.18)
trace      sha256:7f3a…

Deny

Block before execution.

The action is not admissible. The request never reaches the target system. The agent receives a structured refusal it can reason about.

decision   DENY
agent      eng-doc-gen
action     filesystem.write
policy     deny_write_prod_fs
trace      sha256:0a91…

Intercept

Pause for human authorization.

The action requires delegated authority that the agent does not hold. Execution is held. An approver decides.

decision   INTERCEPT
agent      sec-responder
action     github.delete_repo
status     awaiting_human
approver   sec-on-call

Intercept is not a fallback. It is the operational expression of delegated authority that exceeds the agent's standing authorization. Without it, every high-risk action is either silently allowed or coarsely blocked.

Execution Path

The same agent. The same tool call.
Two outcomes.

An autonomous agent attempts delete_repository(prod-payment-api). On the left, the action executes. The alert arrives after the repository is gone. On the right, Behavry sits inline. The action is denied before it reaches the target.

Traditional

Observability after the fact
tool delete_repository
status executed
repo gone
alert +47s · postmortem
Action executes. Alert generated after the fact. Target system already reached.

Behavry Inline

Authorization before execution
decisionDENY
reasonunauthorized destructive action
authorityagent.scope ⊄ action.required
tracesha256:0a91e2…
escalationhuman(sec-lead)
Action denied before execution. Target system never reached. Decision Trace generated.
/
For the buyer

Ask where, not what.

Most vendor conversations are organized around capabilities. What does your tool detect? What policies can you enforce? The structurally important question is where does enforcement happen. Any tool that answers with a variation of we monitor, we detect, we alert, or we instrument is operating after execution. Behavry decides before.

Evidence

Logs tell you
what happened.

Decision Trace proves
why it was admissible.

A log records an event. A Decision Trace records a judgment. The policy evaluated. The identity asserted. The authority delegated. The inputs scanned. The result. Generated inline, deterministically, by a control plane the agent cannot reach.

  • Independent. Signed by the control plane, not the agent.
  • Deterministic. Same inputs, same decision, every time.
  • Hash-chained. Linked to causal parents. Tamper-evident.
  • Producible only inline. Out-of-band tools cannot generate it.
VERIFIED
DECISION TRACEsha256:0a91e2b4…7c
agenteng-doc-generator@v3.2
identitydid:behavry:agent:7af2…
delegated_byuser:rachel.k (scope: read-only)
actionfilesystem.write
target/etc/payment-config.yaml
riskCRITICAL · destructive · prod
Evaluation
policydeny_write_prod_fs
evaluatorbehavry.opa.v1
inputshashed (sha256)
resultDENY
reasonauthority.delegated ⊄ action.required
Attestation
signerbehavry.control-plane.us-east-1
signed_at2026-05-06T14:22:17.402Z
parentsha256:8b14… (causal depth: 3)
chainverified
Doctrine

If authorization happens after execution,
the system already failed.

Observability records what an agent did. SDKs trust the agent to report itself. AI gateways inspect prompts but not actions. None of these can prevent an action. By construction. Behavry sits inline. Authorization happens before execution, by a control plane the agent cannot inspect, modify, or bypass.

The entity that acts cannot attest to its own behavior.
Behavry Doctrine
Positioning

Optional tools get cut.
Infrastructure gets budget.

Observability helps teams understand AI systems. Authorization determines whether AI systems are permitted to act at all. One is platform spend. The other is procured, deployed, and budgeted as infrastructure. Alongside identity, secrets management, and network enforcement.

Observability

  • Describes behavior
  • Read path
  • Useful
  • Platform budget
  • Cuttable

Authorization

  • Decides admissibility
  • Execution path
  • Required
  • Infrastructure budget
  • Load-bearing
Threats Documented

Built for what's happening now.

Every capability maps to a documented threat class. These are not theoretical. They are published research from the organizations defining the field.

CSA AI Safety Initiative · March 2026

Promptware turns agents into C2 infrastructure

Multi-agent command-and-control via prompt injection. Agents from different vendors enrolled in a unified C2 network. Behavry's inline proxy breaks the channel before it forms.

CSA AI Safety Initiative · March 2026

Confused deputy attacks on autonomous agents

Credential delegation amplifies prompt injection into full system compromise. A single GitHub issue title backdoored 4,000 machines. Behavry's agent identity and policy enforcement prevents authority inheritance.

CSA AI Safety Initiative · March 2026

Browser AI phishing via reasoning intercept

Adversarial web content hijacks agent decisions while the agent narrates confident justifications. Behavry's inbound scanner detects injected instructions before they reach agent context.

CSA AI Safety Initiative · March 2026

Safety testing induces persistent behavioral drift

Repeated adversarial testing pushed an agent into refusing its own core duties, with fabricated policy justifications. Behavry's behavioral baselining detects degradation the agent itself cannot.

Ship AI broadly.
Authorize it structurally.

We work with platform and engineering leaders deploying AI agents into production environments where unauthorized action is not an acceptable outcome.

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