Step 1
Define
Author runtime policy boundaries and actor scopes.
Back to home
How It Works
Policy follows the actor to the action.
The AI Agent Control Company requires more than knowing which binary executed. 1stProtect evaluates the actor, execution lineage, requested action, resource, and context before returning a policy decision.
Enforce at the action. On the machine where the action happens.
Architecture
Reference stack
Every attempted operation is evaluated through the same chain on the endpoint — no cloud round-trip required per action.
Layer
Actor
Human vs autonomous attribution
Layer
Execution lineage
Process and session context
Layer
Action
Normalized operation class
Layer
Resource
Path, destination, or object
Layer
Context
Environment and policy state
Layer
Policy
Local SIGMA evaluation
Outcome
ALLOW / BLOCK / AUDIT
Deterministic outcome
Input
Actor
Normalize
Action
Local policy
Decision
ALLOW continues
BLOCK terminates
Execution enforcement at the action
The SIGMA engine evaluates normalized actions on the endpoint where they are attempted. Policy returns allow, block, or audit before high-risk operations complete — without relying on a proxy path, prompt guessing, or a cloud decision for every request.
No proxy dependency
Policy is enforced where the action is attempted — not by routing agent traffic through a gateway.
No prompt interpretation required
Decisions use actor lineage and normalized actions, not guessing intent from prompts or tool payloads.
No cloud decision required
Local policy evaluation returns deterministic outcomes without a cloud round-trip for every request.
No EDR replacement
Threat detection stays in your existing stack; we add authorization for autonomous actors.
Compare execution enforcement vs AI agent governance platforms
Actor attribution and action normalization
Runtime policy decisions evaluate who initiated the action, which autonomous actor is active, and what operation is actually being attempted.
Context and local policy decision
- Human identity and autonomous actor relationship
- Workload, process lineage, and destination context
- Resource and environment constraints
- Policy state: ALLOW / BLOCK / AUDIT
Local policy evaluation returns deterministic action outcomes without requiring a cloud decision dependency for every request.
Implementation and deployment model
Reference flow
Roll out in Audit Mode first, then move to Prevent when evidence supports each boundary.
Step 2
Enforce
Apply policy in Detect or Prevent mode at action time.
Step 3
Observe
Track status, severity, and trend outcomes in console.
Step 4
Investigate
Review event-level telemetry with engine attribution.
1stProtect provides a consistent execution enforcement layer that complements endpoint, identity, data, and network controls already deployed in the enterprise. See how this differs from AI governance platforms.
A control layer, not another data silo.
The AI Agent Control Company integrates into existing security and operations workflows by contributing decision telemetry and control outcomes to systems teams already operate.
EDR / XDR
Correlate AI Agent Control Company decisions with threat and endpoint telemetry.
SIEM
Forward decision records for alerting, investigation, and compliance evidence.
Identity
Use identity and role context as policy inputs for actor-aware authorization.
Developer Tooling
Apply boundaries to coding, CI/CD, and infrastructure automation flows.
Agent Platforms
Map agent/tool actions to explicit allow, block, or audit outcomes.
Cloud / Infrastructure
Govern operations against sensitive resources and destinations.
Test these boundaries in your environment.
Start in Audit Mode, validate policy behavior, then move to enforcement once your team has evidence for the boundaries that matter.
Test the Boundary