The question we're answering
“What risks are introduced by our AI systems and data flows?” AI systems introduce risk in places traditional security reviews don't look: what data reaches a third-party model, what a model can infer beyond its intended use, and what happens when a provider's own security posture changes.
Who this is for
Organizations that have deployed or are about to deploy AI systems and need a real risk picture before an incident, a customer questionnaire, or a board review forces the question.
What this covers
This assessment traces risk through the full AI system, not just the model itself:
- Data flow mapping: what data reaches which AI system, and where it goes from there
- Data classification: sensitivity of the data actually exposed to each AI system
- Third-party and provider dependencies: what you're trusting a vendor's model or API with
- Authorization boundaries: who and what can trigger AI-driven actions
- Testing: how the risk assessment is validated, not just documented
Outcomes & deliverables
- A risk-tiered inventory of AI systems and the data flows behind them
- Clear identification of third-party and provider dependencies
- A prioritized remediation plan mapped to actual exposure
- A repeatable process for assessing the next AI system before it launches
How we approach it
Inventory AI systems and trace the data flows through each one.
Score risk against real exposure, not a generic AI-risk template.
Prioritized findings and a remediation plan your team can execute.
Regulatory readiness
Governance work here feeds directly into regulatory evidence, not just internal policy.
Industries & use cases
Proof
50 active enterprise clients, 100+ SMB clients, and 1,000+ assessments delivered per year: this isn't our first engagement like yours.
Expert reviewer
