How AI-Governed Operations Work
AI manages internal operations like alert correlation, ticket assignment, status updates, and investigation notes, while surfacing step-by-step guidance for any actions that require analyst execution in external environments.
Core Execution Principles
Internal AI Operations
AI handles internal platform work like assigning tickets, merging correlated alerts, updating case status, and adding investigation notes so analysts focus on decisions, not busywork.
- Automated alert correlation and assignment
- Case status and note management
- Internal workflow orchestration
Analyst-Controlled External Actions
AI never executes actions against external customer environments. It surfaces structured remediation guidance and step-by-step instructions. Analysts decide what gets executed externally.
- Step-by-step remediation guidance
- Prioritized response recommendations
- Analyst-authorized external execution only
Full Audit Trail
Every investigation step, AI recommendation, and analyst decision is logged. Compliance teams and incident responders have a clear record of findings, guidance provided, and actions taken.
- Investigation activity logging
- Decision audit trail
- Regulatory-ready records
From Investigation to
Controlled Action
AI does not execute actions directly. Instead, the platform analyzes correlated evidence, surfaces prioritized findings, and delivers step-by-step remediation instructions that analysts review and act on.
- AI correlates signals and identifies threat
- Platform surfaces findings and recommended steps
- Analyst reviews guidance and context
- Analyst executes remediation as instructed
AI correlates threat signals
Cross-tool evidence analysis
Findings surfaced
Prioritized recommendations delivered
Analyst reviews guidance
Context and instructions evaluated
Analyst takes action
Remediation executed by operator
Activity logged
Investigation trail recorded
Policy Enforcement
Layers
AI guidance is structured at every layer. Operational context anchors all recommendations. The platform surfaces findings and instructions while analysts retain full authority over what gets executed.
Layer 3
Execution Engine
Analyst-executed actions guided by AI-provided instructions
Layer 2
Policy Evaluation
Authorization, scope validation, and rule enforcement
Layer 1 · Foundation
Operational Context
Investigation state, analyst role, and environment conditions
Verifiable Operational
Execution
Every AI-generated finding, recommendation, and analyst decision is logged. These records provide security teams with a complete and defensible history of investigation activity.
Investigation Records
Every AI finding, recommendation, and analyst decision is logged with full context: what was surfaced, when, and what the analyst decided.
Chain-of-Custody
Every recommendation is linked to the originating investigation context, providing a traceable line from signal to analyst action.
Regulatory Evidence
Investigation histories can be exported for compliance review, incident response reporting, or legal hold.