Know where AI creates leverage—and where it creates exposure
A focused assessment of your AI assets, hybrid delivery pipelines, software supply chain, and policy controls.
The question this engagement answers
Where should we invest first to improve AI delivery speed without increasing operational or security risk?
Many organizations have overlapping AI pilots, security scanners, CI/CD platforms, and cloud controls—but no shared model of how they interact. We build that model, identify the highest-consequence gaps, and turn the findings into a sequenced plan.
What we assess
- AI estate: agents, models, prompts, plugins, external model calls, owners, and sensitive data paths
- Delivery systems: source control, build runners, registries, deployment platforms, and runtime environments
- Software supply chain: dependencies, secrets, SBOMs, provenance, signing, and artifact promotion
- Control model: identities, permissions, OPA policies, quality gates, exceptions, and human approvals
- Operations: telemetry, alert flow, incident patterns, rollback paths, and remediation bottlenecks
What you receive
- Current-state architecture and trust-boundary map
- AI asset and tool-exposure inventory
- Risk-ranked findings with evidence and business impact
- Target-state governance and control blueprint
- A 30/60/90-day implementation roadmap
- Executive readout and technical working session
A useful starting point when
- AI projects are moving toward production but governance is still being defined.
- Security findings are growing faster than remediation capacity.
- Cloud and on-premise pipelines enforce different rules.
- Leadership needs a defensible investment sequence before selecting more tools.