Shrink the gap between finding a vulnerability and shipping a safe fix
Design remediation workflows where AI handles analysis and repetitive patch work, while tests, policy, and human approvals govern production impact.
From alert to governed action
Autonomous remediation should not mean unrestricted production access. We design a bounded workflow in which an AI agent can accelerate the parts it is good at while deterministic systems remain the source of authority.
- Detect and contextualize. Correlate a vulnerability with affected artifacts, runtime exposure, dependency paths, and business criticality.
- Propose a targeted change. Create an isolated branch or configuration change with evidence linking the fix to the finding.
- Validate deterministically. Run unit, integration, security, compatibility, and deployment tests. Failed evidence blocks promotion.
- Evaluate policy. OPA checks repository class, agent identity, data boundary, change risk, and approval requirements.
- Route or release. Low-risk changes may proceed through a defined automation path; high-consequence systems receive a prepared patch and human approval request.
- Observe and recover. Canary telemetry and rollback criteria constrain production impact and feed learning back into the workflow.
Hybrid example
A vulnerable package appears in both an internet-facing cloud service and an isolated on-premise system. Contextual triage prioritizes the exposed service. The agent prepares fixes for both, but policy allows automated progression only for the low-risk cloud repository. The on-premise system receives the tested patch, evidence bundle, and an emergency approval task.
What we implement
- Vulnerability context and prioritization workflow
- Agent identities and least-privilege repository access
- Patch-generation sandbox and branch controls
- Deterministic validation gates
- OPA policies for risk-based approvals
- Canary, rollback, audit, and exception runbooks
- Baseline metrics for remediation flow and alert quality
Success is measured, not promised
We establish current-state baselines, then measure time-to-triage, time-to-ready-patch, policy rejection rate, test failure rate, human review load, and production rollback frequency. Automation expands only when the evidence supports it.