AI exposure and governance summary
Grounded in the evidence, testing and risk context collected during this engagement.
Emerging technology
AI Security and Shadow AI Risk Audit
Discover how AI is actually being used, where sensitive information can escape, and whether approved AI systems resist misuse and manipulation.
The business problem
Why this assessment matters.
Employees adopt public AI tools faster than policy and procurement processes can respond. At the same time, sanctioned AI applications introduce new data flows, model dependencies, prompt-based attacks and opaque third-party processing.
Our audit separates workforce use, embedded AI features and custom AI systems. Each is evaluated for data exposure, access, technical abuse, supplier dependencies and governance appropriate to its impact.
Audit coverage
What we assess.
Final scope reflects your environment, critical systems, risk profile and assurance objective.
Approved AI inventory and business ownership
Shadow AI discovery and unsanctioned use
Sensitive-data input, retention and training terms
AI identities, permissions and secrets
Prompt injection and insecure output handling
Model, plugin, agent and retrieval dependencies
AI vendor due diligence and contract controls
Monitoring, human oversight and incident response
Assessment process
How the audit works.
Testing is evidence-led, risk-based and designed to protect business operations.
- Map known AI use cases, data flows and critical decisions
- Discover representative unsanctioned tools and access patterns
- Review approved system architecture, permissions and vendor terms
- Test scoped AI abuse cases with controlled content
- Prioritize guardrails by data sensitivity and decision impact
Engagement outputs
What your team receives.
Approved and shadow AI inventory findings
Grounded in the evidence, testing and risk context collected during this engagement.
Sensitive-data flow assessment
Grounded in the evidence, testing and risk context collected during this engagement.
AI application security findings
Grounded in the evidence, testing and risk context collected during this engagement.
Supplier and contract risk analysis
Grounded in the evidence, testing and risk context collected during this engagement.
Prioritized AI guardrail roadmap
Grounded in the evidence, testing and risk context collected during this engagement.
Standards and guidance
A recognizable basis for conclusions.
Applicable standards organize testing and reporting while the actual environment determines risk.
NIST AI Risk Management Framework
OWASP Top 10 for LLM Applications
ISO 42001 concepts
MITRE ATLAS
NIST CSF 2.0
Frequently asked questions
Planning your ai security and shadow ai assessment.
Can you discover every shadow AI tool?
No single source is complete. We combine stakeholder discovery, approved telemetry and SaaS or network evidence to build a defensible risk-based view.
Will you ban employee AI use?
The objective is proportionate governance. Recommendations distinguish prohibited data and uses from approved tools and productive low-risk use cases.
Can you test a custom AI application?
Yes. Scope can include prompt injection, authorization, retrieval boundaries, output handling, secrets and connected tools.
Do you review AI vendor terms?
We assess relevant security, retention, model-training, subprocessors and incident terms, with final legal interpretation left to counsel.
Does this cover AI governance regulation?
We can map security and governance evidence to selected requirements, while the audit remains focused on cybersecurity and operational risk.
Related assessments
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Request a AI Security and Shadow AI audit
Discuss scope, timing, access requirements and the evidence your stakeholders need.