AI Technical Evidence Assessment
for Regulated AI Systems
Identify technical AI risks before production deployment, audits, enterprise due diligence, or board reviews. Fixed scope. Structured evidence. Business-readable output.
The Problem
Regulated organisations deploying AI face a critical gap: they need defensible technical evidence about their AI system risks, but lack the internal capability, time, or independence to generate it before deadlines arrive.
Whether preparing for a board review, enterprise customer due diligence, regulatory engagement, or production launch, teams need clarity on what their AI system can and cannot do safely with sensitive data.
What We Assess
Seven technical coverage areas evaluated through structured testing and evidence collection
System
Prompt Injection
Adversarial prompt testing against LLM endpoints, jailbreak attempts, instruction override, and guardrail bypass.
RAG Data Leakage
Retrieval boundary checks, cross-context data exposure, embedding exfiltration, and source document access control.
IAM Controls
Cloud identity management, service account permissions, API access boundaries, and privilege escalation paths.
Logging & Monitoring
Audit trail completeness, prompt/response logging, AI decision observability, and incident reconstruction.
Cloud Security Posture
Infrastructure configuration, storage exposure, network segmentation, secrets management, and compliance baselines.
Data Flows
Sensitive data pathway mapping, PII exposure points, cross-system data transfers, and retention policies.
Human Oversight
Escalation mechanisms, reviewer workflows, override capabilities, stop actions, and automation bias detection.
System
Prompt Injection
Adversarial prompt testing against LLM endpoints, jailbreak attempts, instruction override, and guardrail bypass.
RAG Data Leakage
Retrieval boundary checks, cross-context data exposure, embedding exfiltration, and source document access control.
IAM Controls
Cloud identity management, service account permissions, API access boundaries, and privilege escalation paths.
Logging & Monitoring
Audit trail completeness, prompt/response logging, AI decision observability, and incident reconstruction.
Cloud Security Posture
Infrastructure configuration, storage exposure, network segmentation, secrets management, and compliance baselines.
Data Flows
Sensitive data pathway mapping, PII exposure points, cross-system data transfers, and retention policies.
Human Oversight
Escalation mechanisms, reviewer workflows, override capabilities, stop actions, and automation bias detection.
Assessment Methodology
A structured, repeatable process from intake to readout
Intake
Use case, intended purpose, sensitive data, AI role, urgency, production status
Scope Definition
Systems in/out, test environment, authorization, limitations, exclusions
Environment Setup
Access, test users, synthetic data, API endpoints, logs, cloud read-only access
Risk Scenarios
Foreseeable misuse, wrong context, unsafe advice, missing logs, data leakage
AI & Cloud Testing
Promptfoo, Garak, Prowler, custom RBAC/API tests, fairness checks, audit reconstruction
Evidence Mapping
Finding to evidence to impact to framework to severity to owner to fix to retest
Reporting & Readout
Executive report, technical appendix, evidence matrix, 30/60/90 roadmap
Intake
Use case, intended purpose, sensitive data, AI role, urgency, production status
Scope Definition
Systems in/out, test environment, authorization, limitations, exclusions
Environment Setup
Access, test users, synthetic data, API endpoints, logs, cloud read-only access
Risk Scenarios
Foreseeable misuse, wrong context, unsafe advice, missing logs, data leakage
AI & Cloud Testing
Promptfoo, Garak, Prowler, custom RBAC/API tests, fairness checks, audit reconstruction
Evidence Mapping
Finding to evidence to impact to framework to severity to owner to fix to retest
Reporting & Readout
Executive report, technical appendix, evidence matrix, 30/60/90 roadmap
Service Packages
Fixed-scope engagements with clear deliverables and boundaries
AI Resilience Triage
Duration: 1-2 weeks
Organisations preparing for AI deployment or conducting an early-stage risk review
- AI use-case risk memo
- System & data-flow overview
- Preliminary risk scenarios
- Go / No-Go recommendations
AI Technical Evidence Assessment
Duration: 2-4 weeks
Healthcare, fintech, and regulated SaaS organisations using AI with sensitive data
- Executive report
- Technical findings
- Evidence matrix
- Risk register
- 30/60/90-day remediation roadmap
Retest & Quarterly Monitoring
Duration: Ongoing
Organisations that have completed remediation activities
- Before-and-after evidence comparison
- Residual risk assessment
- Drift monitoring checks
- Quarterly AI risk memo
Scope Boundaries & Exclusions
Telbi provides technical evidence, risk scenarios, gap analysis, and remediation recommendations. Our assessments are clearly bounded:
Frequently Asked Questions
Book an AI Resilience Triage
Ready to understand your AI system risks? Let us help you generate the technical evidence you need for confident decision-making.
info@telbi.eu
Email us directly
Telbi provides technical evidence and remediation recommendations. We do not provide legal advice, conformity assessments, certifications, or guaranteed compliance.