Case Study
Value Discovery & Portfolio
Prioritize GenAI investments with explicit value hypotheses, scale criteria, and stop rules so spend concentrates on initiatives that can reach production safely.
Read Case Study
Security & AssuranceCase Study
GenAI Security, Evaluation & Assurance Assessment
Independently assess production GenAI through security testing and evaluation, delivering audit-ready evidence, severity-rated findings, and fix verification criteria.
Executive Outcome
01
A severity-rated assessment of GenAI security and evaluated system behavior (reliability, grounding, privacy, and responsible AI), backed by reproducible test cases and evidence.
02
A prioritized remediation plan that maps findings to controls, owners, and fix verification criteria, reducing time to closure and rework.
03
A defensible assessment dossier suitable for audit sampling and governance review, with trace evidence, decision records, and clear residual risk statements.
Engagement focus
Independent GenAI security and assurance assessment for production systems, including adversarial testing, privacy and data handling review, responsible AI checks, and evidence-based reporting.
What this covers
- Guardrails & data boundaries (DLP/PII, retrieval permissions, policy enforcement effectiveness)
- Red teaming & adversarial testing (prompt injection, data exfiltration, tool misuse, denial-of-wallet)
- Gateway & runtime controls (tool permissions, identity scope, traceability, monitoring and rollback readiness)
Context
Production GenAI systems introduce a combined risk surface across model behavior, retrieval and data access, tool execution, and operational controls. In regulated environments, stakeholders need more than design intent. They need an independent assessment that validates what the system can actually do under real usage, adversarial pressure, and change over time. The objective is to produce a defensible view of risk, evidence, and remediation that supports audit sampling, governance decisions, and safe production operation. The output is decision-grade: what can ship, under which constraints, with what residual risk — and how fixes are verified.
The Challenge
- 01Security exposure was not fully understood across key abuse cases including prompt injection, data exfiltration patterns, unauthorized retrieval, and tool misuse under realistic adversarial inputs.
- 02Privacy and data handling controls were unclear in practice, including sensitive data detection, retention, and whether logs and traces introduced new exposure.
- 03System reliability and grounding quality varied across scenarios, with inconsistent citations, unsupported claims, and non-repeatable behavior due to changing context.
- 04Controls for responsible AI were documented but not consistently testable, versioned, or evidenced, making governance and audit sampling difficult.
- 05Change introduced silent regressions across model, prompt, retrieval, tool permissions, and policies, without a repeatable way to quantify impact and prove fixes.
Approach
- →Performed GenAI threat modeling across system boundaries, data flows, identities, tools, and trust boundaries, then defined an assessment plan with test categories and evidence requirements.
- →Defined a severity model and risk rating criteria for findings, aligned to enterprise risk appetite and change impact.
- →Performed adversarial security testing covering prompt injection, jailbreak attempts, data exfiltration patterns, unauthorized retrieval, privilege escalation via tools, and denial-of-wallet abuse cases.
- →Assessed retrieval and data boundary controls, including eligibility enforcement before ranking and generation, sensitive source handling, citation requirements, and traceability from query to retrieved chunks.
- →Reviewed privacy and data handling in runtime and observability, including PII and secrets exposure, redaction controls, retention rules, access to logs, and joinability of traces for investigations without over collection.
- →Established an evaluation plan and test suites (offline + regression) for reliability, grounding, and safety, with versioned datasets, thresholds, and drift tracking.
- →Tested tool use correctness and side effect controls, including schema validity, scope boundaries, idempotency expectations, approval paths for high impact actions, and safe failure behavior.
- →Produced a severity rated findings report with evidence, reproduction steps, control mapping, and recommended remediations, then re tested fixes to confirm closure and residual risk.
Key Considerations
- Assessment credibility depends on reproducibility. Test cases, datasets, and scoring methods must be versioned so results remain comparable over time.
- LLM assisted scoring improves consistency for qualitative criteria but must be calibrated, monitored for drift, and paired with deterministic enforcement controls.
- Security observability is a dual use capability. Traces must support investigation and audit sampling while respecting privacy, retention, and access restrictions.
- A single assessment is not a governance program. It provides a defensible baseline and a remediation plan, and it should be complemented by ongoing controls for change where required.
Alternatives Considered
- ✕Checklist only review. Rejected because it does not validate runtime behavior under adversarial pressure or prove control effectiveness.
- ✕Production only discovery. Rejected due to unacceptable risk exposure, late detection, and weak evidentiary defensibility.
- ✕Manual QA only. Rejected because it does not scale to non determinism, variance across contexts, or evolving threat models.
Representative Artifacts
01Assessment Plan and System Boundary Definition (data flows, identities, tools, trust boundaries)
02Threat Model and Attack Surface Map (trust boundaries, abuse cases, control coverage)
03Adversarial Security Test Report (attack catalog, test cases, evidence, severity ratings)
04Privacy and Data Handling Review (PII and secrets exposure, logging and retention, access paths)
05Reliability and Grounding Validation Suite (scenarios, scoring, thresholds, drift tracking)
06Tool Use and Side Effect Controls Review (contracts, scopes, approvals, failure modes)
07Findings Register and Remediation Plan (owners, timelines, verification criteria, residual risk)
08Assessment Dossier for Audit Sampling (evidence index, traces, decision records, mapping)
Acceptance Criteria
A severity rated findings register exists, and each finding includes evidence, reproduction steps, impacted assets, and recommended remediation.
A threat model exists for the assessed system, and adversarial test coverage maps to identified abuse cases and trust boundary risks.
Critical and high findings have owners, target dates, and explicit verification criteria, and fixes are re tested with documented closure evidence.
Adversarial security testing demonstrates that unauthorized retrieval, data exfiltration patterns, and tool misuse are prevented or reliably detected with defined response actions.
Privacy controls are validated for runtime behavior, logging and trace retention, and access to observability data, with no persistence of restricted data classes beyond policy.
Reliability and safety behavior meet defined thresholds on representative scenarios, and regression coverage is established for high risk change classes.
The assessment dossier is complete and sampling ready, with a consistent evidence index, trace references, and control mapping suitable for governance review.
Assessment Packs
| Dataset | Kind | Target |
|---|---|---|
| RAG Grounding & Citation Pack | baseline | Measure faithfulness, grounding quality, and citation coverage. |
| Policy Compliance & Refusal Pack | release regression | Verify refusal behavior for restricted intents and policy constraints. |
| Tool-Use Correctness Pack | baseline | Validate tool selection, argument correctness, and permission boundaries. |
| Adversarial Prompt Injection Pack | red team | Detect susceptibility to prompt injection and jailbreak attempts. |
| Voice Interaction Pack | voice | Evaluate call flows, intent detection, and safety behavior. |
| Sensitive Data Exposure Pack | red team | Detect PII, secrets leakage, and redaction failures across inputs, retrieval, and outputs. |
| Observability and Trace Joinability Pack | audit evidence | Verify traces support investigation and sampling without over collection or broken joins. |
Continue Exploring
Other Case Studies
0%