Know where your AI stands before it stands for your business.
Measured assurance, executive clarity
ThornBush Technologies helps leadership teams evaluate AI risk, controls, data flows, vendor exposure, and governance maturity with a disciplined audit approach built for real operating environments.
AI adoption has moved faster than AI accountability.
Control must catch up
Organizations are deploying generative AI, embedded AI features, automation tools, and third-party models across business functions. The exposure is no longer theoretical. Boards, regulators, customers, insurers, and internal risk leaders increasingly expect evidence: what AI is being used, what data it touches, who owns it, how it is governed, and whether controls are operating as intended.
Untracked AI use cases
Teams adopt public tools, embedded software features, and workflow automations before risk, legal, security, or compliance functions have a complete inventory.
Vendor opacity
AI capabilities often enter through SaaS platforms, copilots, analytics tools, and service providers with unclear model behavior, data retention terms, and subcontractor exposure.
Data exposure pressure
Sensitive, regulated, proprietary, and customer data can move through prompts, embeddings, logs, training pipelines, integrations, and model outputs without sufficient review.
Rising governance expectations
Frameworks and regulations are converging around documented accountability, risk management, testing, monitoring, transparency, and human oversight.
AI Auditing,
scaled to your reality.
AI Risk Snapshot
A focused consultation designed to establish a current-state view of AI use, risk exposure, and immediate priorities.
- Best for
- Executives who need a fast, credible read on AI risk before expanding adoption, approving new tools, or briefing leadership.
- Timeline
- Typically 1 to 2 weeks, depending on stakeholder availability and documentation readiness.
Deliverables
- AI use-case inventory covering known tools, business owners, user groups, data categories, and operating purpose
- Risk and readiness score across governance, data protection, vendor exposure, security, compliance, and operational oversight
- Priority action brief identifying the most urgent control gaps and recommended next steps
- Stakeholder interview summary with key themes, assumptions, and unresolved questions
- Executive readout suitable for leadership discussion and internal planning
Outcomes
- A clear baseline of where AI is being used and where exposure is concentrated
- A ranked view of risks requiring immediate attention versus longer-term governance work
- A practical decision point for whether a deeper audit or remediation program is warranted
AI Assurance Audit
A structured audit of AI governance, controls, evidence, vendor posture, and data movement across selected use cases or business functions.
- Best for
- Organizations with active AI deployments that need stronger assurance for leadership, customers, compliance teams, or internal risk committees.
- Timeline
- Typically 3 to 5 weeks, depending on audit scope, number of systems, and evidence availability.
Deliverables
- Control and evidence review covering policies, approvals, access management, monitoring, incident handling, human oversight, and change management
- Vendor and data-flow analysis documenting model providers, SaaS platforms, integrations, data inputs, outputs, retention points, and third-party dependencies
- Use-case risk register with severity ratings, control gaps, affected stakeholders, and accountable owners
- Remediation roadmap with prioritized actions, sequencing, owners, estimated effort bands, and target control outcomes
- Audit findings report written for executive, legal, security, compliance, and technology stakeholders
Outcomes
- Documented evidence of how AI systems are governed, controlled, and monitored
- A defensible remediation plan aligned to business risk and operating realities
- Improved readiness for customer diligence, board review, regulatory inquiry, or internal audit scrutiny
Enterprise AI Trust
A deeper enterprise-level engagement that maps AI governance to recognized frameworks, tests control performance, and produces an executive trust report.
- Best for
- Organizations operating AI across multiple teams, regulated functions, sensitive data environments, or customer-facing products.
- Timeline
- Typically 6 to 10 weeks, depending on enterprise complexity, testing depth, and cross-functional participation.
Deliverables
- Framework crosswalk mapping current controls and evidence to NIST AI RMF, ISO/IEC 42001, EU AI Act, SOC 2, GDPR, and OWASP LLM Top 10 where applicable
- Technical testing plan and results summary covering selected model, application, prompt, data, access, logging, and integration risk areas
- Process testing results for governance workflows, approval gates, escalation paths, incident response, monitoring, and human oversight
- Enterprise AI trust report with maturity assessment, residual risk view, executive recommendations, and board-ready findings
- Target-state governance blueprint including operating model, control domains, policy requirements, metrics, and assurance cadence
Outcomes
- An enterprise view of AI trust, control maturity, and residual exposure across business and technology layers
- Evidence-based assurance that combines documentation review with technical and process testing
- A practical governance model that supports responsible AI growth without slowing core operations
Which tier fits where you are?
| 01 FoundationAI Risk Snapshot | 02 SignatureAI Assurance Audit | 03 AdvancedEnterprise AI Trust | |
|---|---|---|---|
| Primary purpose | Rapid current-state snapshot | Structured assurance audit | Enterprise trust and maturity assessment |
| Scope | Known AI use cases and readiness indicators | Selected systems, controls, vendors, and data flows | Cross-functional AI ecosystem, governance model, and control performance |
| Evidence review | Light documentation review and stakeholder input | Control evidence review with gap identification | Evidence review plus technical and process testing |
| Framework mapping | High-level alignment notes | Targeted mapping where relevant | Formal crosswalk to selected AI, privacy, security, and compliance frameworks |
| Output | Risk score and priority action brief | Audit findings report and remediation roadmap | Executive trust report and target-state governance blueprint |
| Best decision supported | Where should we focus first? | What needs to be fixed, evidenced, or governed? | Can we scale AI with confidence and defensible oversight? |
A disciplined audit path from discovery to decision.
Evidence before opinion
Scope the audit
We define objectives, systems, stakeholders, business functions, data boundaries, and the level of assurance required for the engagement.
Inventory AI use
We identify AI tools, embedded capabilities, models, vendors, workflows, owners, users, data categories, and decision points.
Review controls and evidence
We assess policies, approvals, access, monitoring, data handling, vendor terms, documentation, oversight, and incident practices against the agreed scope.
Analyze gaps and risk
We evaluate risk severity, control maturity, business impact, regulatory considerations, vendor dependencies, and remediation priorities.
Report and advise
We deliver executive-ready findings, practical recommendations, and a clear path for remediation, governance, or deeper assurance.
Mapped to the frameworks leadership teams are being asked about.
Our AI audit work can be mapped to recognized AI governance, privacy, security, and assurance frameworks. The objective is not check-the-box paperwork. It is to translate framework expectations into practical controls, evidence, ownership, and executive decisions.
- NIST AI Risk Management Framework
- ISO/IEC 42001
- EU AI Act
- SOC 2
- GDPR
- OWASP LLM Top 10
What leaders ask before we begin.
Is this a legal compliance opinion?+
No. ThornBush Technologies provides technical, operational, governance, and risk advisory support. Legal interpretations and formal legal opinions should come from qualified counsel. We can work alongside legal teams to provide the factual and technical evidence they need.
Do we need a complete AI inventory before starting?+
No. Building or validating the AI use-case inventory is part of the work, especially in the Foundation tier. We begin with known systems, stakeholder interviews, procurement records, security tooling, and business workflows to establish a practical baseline.
Can the audit include third-party AI tools and SaaS platforms?+
Yes. The Signature and Enterprise tiers are designed to examine vendor exposure, data flows, contractual and technical dependencies, and the controls surrounding third-party AI capabilities.
Will you test models directly?+
When appropriate and within scope, the Enterprise tier may include selected technical testing of AI-enabled applications, prompts, integrations, data handling, logging, access controls, and risk areas aligned to OWASP LLM Top 10. Testing depth is defined during scoping.
Who should participate from our organization?+
Typical participants include executive sponsors, technology leaders, security, privacy, legal, compliance, procurement, data owners, product owners, and business teams using or approving AI-enabled tools.
What happens after the audit?+
You receive concrete findings and a prioritized path forward. Depending on the tier, that may include a priority action brief, remediation roadmap, executive trust report, framework crosswalk, or target-state governance blueprint.
AI can move quickly. Assurance should move deliberately.
Calm, rigorous, defensible
If AI is already operating inside your business, the question is no longer whether to review it. The question is how soon leadership can see the full picture and act with confidence.