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GenAI SecurityCase Study
Generative AI Security by Design
Secure production GenAI through enforced data boundaries, permissioned retrieval, and governed tool access. Add runtime guardrails and security-grade observability to prevent leakage and unauthorized actions.
Executive Outcome
01
Leakage risk reduced through contract-governed, permissioned retrieval with eligibility enforced before ranking and generation.
02
Runtime behavior controlled via enforced guardrails, grounding policies, and tool constraints that prevent unauthorized actions.
03
Security incident readiness enabled through end-to-end security telemetry and traceability from request to retrieval to response.
Engagement focus
Production-grade GenAI security for assistants and RAG systems focused on data boundaries, permissioned retrieval, governed tool use, runtime guardrails, and security observability.
Context
Enterprises often start with “chat with documents” demos and quickly discover that production assistants require security controls beyond model behavior. The primary constraint is not model capability, but control over what the system is allowed to access, what it is allowed to claim, and how it stays grounded to eligible evidence. A secure GenAI system must prevent unauthorized retrieval and disclosure, enforce policy at runtime, and produce traceable evidence for audits and incident response while meeting latency and cost budgets.
The Challenge
- 01Datasets and knowledge sources entered production with inconsistent quality, lineage, and sensitivity checks, increasing leakage and stale-context risk.
- 02Retrieval was treated as a black box with limited visibility into why content was selected and whether it was eligible for the user and the use case.
- 03Answers lacked consistent citations and grounding, and some responses contained unsupported claims or ambiguous attribution.
- 04No systematic runtime enforcement existed to prevent ineligible retrieval, unsafe tool use, or unauthorized disclosure under real usage patterns.
Approach
- →Defined a pre-production dataset and source release gate covering lineage, ownership, sensitivity labeling, expiry, and minimum metadata required for safe indexing.
- →Defined a Retrieval Security Contract governing eligibility, freshness, permission checks, citation requirements, and traceability from query to retrieved chunks.
- →Implemented runtime guardrails focused on enforcement, including input and output policy checks, grounded response constraints, and controlled refusal when evidence is missing, ineligible, stale, or insufficient.
- →Enforced ACLs and eligibility filtering at the retrieval layer so permission checks occur before ranking and generation.
- →Specified security-grade observability, including trace stitching and incident telemetry, so investigations do not depend on manual reconstruction.
Key Considerations
- Pre-production gates require reliable metadata and document lifecycle controls to keep eligibility, freshness, and access rules accurate over time.
- Runtime guardrails add latency and require explicit performance budgets, caching strategies, and fallbacks.
- Security observability must be designed with retention, privacy, and joinability requirements so traces are usable for incident response without creating new exposure.
Alternatives Considered
- ✕Long-context only without retrieval: rejected due to cost, stale-context risk, and lack of auditable source attribution.
- ✕Naive top-K retrieval without eligibility filtering: rejected due to leakage risk, inconsistent answer quality, and weak enforceability of permission boundaries.
- ✕Post-generation filtering only: rejected because it detects issues too late and does not prevent unauthorized retrieval or tool actions.
Representative Artifacts
01Dataset and Source Release Gate (lineage, sensitivity, metadata minimums, expiry, approval criteria)
02Retrieval Security Contract Template (eligibility, freshness, permissions, citations, trace requirements)
03Indexing and Chunking Decision Framework (by document type, metadata and lifecycle requirements)
04Runtime Guardrails Policy Set (input and output checks, refusal rules, tool and permission boundaries)
05Security Observability and Trace Specification (request context, retrieval trace, enforcement decisions, response metadata)
Acceptance Criteria
Users cannot retrieve chunks outside their permissions, and eligibility is enforced before ranking and generation.
The system refuses to answer when evidence is missing, ineligible, stale, or insufficient.
Datasets and sources cannot be promoted to production without minimum metadata, lineage, and sensitivity validation.
Security telemetry supports incident investigation of leakage and unauthorized behavior within defined latency and retention budgets.
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