Project Name
Enterprise AI Made Safe Across 5 Business Units With a Production-Grade Guardrail Framework
A North American technology organisation running AI across multiple business units had moved quickly from experimentation to production without a unified security or governance framework. Users had discovered the AI assistant could be manipulated into producing policy-violating outputs. Unknown CVEs lurked in the production stack. No audit trail existed. All users had equivalent AI access regardless of role. Applying its AI-First approach, Ksolves built a comprehensive AI Security and Governance framework with guardrails, audit trails, and ISO 42001-aligned controls across all five business units from day one.
- Jailbreak and Prompt Injection Exposure: The AI assistant could be manipulated via crafted inputs into producing policy-violating outputs - an unacceptable liability under the company's acceptable-use policy.
- Vulnerable Dependency Chain: Rapid prototyping left unknown CVEs in a tangle of third-party AI libraries and open-source integrations with no systematic vulnerability tracking.
- Zero Auditability: No system existed to log, replay, or explain what the AI said to whom and why - a critical gap when regulators requested evidence of responsible AI use.
- Uncontrolled User Access: All authenticated users had equivalent access to all AI features regardless of role or data sensitivity - violating least-privilege principles.
- No Governance Standard Alignment: No formal mapping to ISO 42001 or equivalent - impossible to demonstrate AI risk management maturity to enterprise clients or auditors.
- CI/CD Security Blind Spot: AI components deployed through a standard pipeline with no AI-specific SAST or DAST checks - security regressions could reach production undetected.
Ksolves designed a layered AI Security and Governance framework addressing risk at every stage of the AI interaction lifecycle. The governing principle was defence in depth: no single control relied upon in isolation, every layer independently auditable, reusable across all five business units without separate deployments.
- LLM Guardrail Layer: Real-time input/output filter detecting and blocking prompt injection, jailbreaks, and policy-violating queries using rule-based classifiers and a secondary safety-tuned gatekeeper model before the primary model responds.
- SAST / DAST Integration: Static and dynamic security analysis embedded in the CI/CD pipeline - every AI deployment scanned for vulnerable dependencies, insecure API patterns, and model-weight integrity before release.
- ISO 42001 Control Mapping: Full mapping of AI system behaviours against ISO 42001 requirements - gap analysis, remediation roadmap, and audit-ready evidence pack produced for regulator submission.
- Role-Based AI Access Controls: Least-privilege access model restricting which user roles can invoke which AI capabilities across 5 business units - finance-sensitive queries blocked from general-user accounts.
- Immutable Audit Log: Append-only log capturing every AI query, model version, context, and response with timestamps and user identifiers - full forensic replay of any AI output.
Technology Stack
| Category | Technology |
|---|---|
| AI/ML | LLM Guardrail Layer |
| DevSecOps | SAST / DAST Scanning |
| Compliance | ISO 42001 Alignment Framework |
| Security | Dependency Audit & Vulnerability Registry |
| Architecture | User Lockdown & Role-Based Access Controls |
| Platform | Audit Log & Explainability Store |
- 100% of Prompt Injection Patterns Blocked: Before: no input filtering - crafted prompts produced harmful outputs in test scenarios. After: 100% of known injection and jailbreak patterns blocked, zero policy-violating outputs in post-deployment QA (target).
- 40+ AI Dependencies Audited and Cleared: Before: 40+ unreviewed third-party AI libraries in production with no CVE tracking. After: unreviewed libraries reduced to zero, high-severity CVEs flagged within 24 hours (target).
- First ISO 42001 Evidence Pack Produced: Before: could not produce a single governance evidence artefact for regulators. After: ISO 42001 gap assessment complete, full evidence pack produced for first formal AI governance audit submission (target).
- Zero Cross-Role Data Exposure Across 5 BUs: Before: all users had unrestricted AI access regardless of role or data sensitivity. After: role-based controls across 5 business units, zero cross-role data exposure in post-deployment review (target).
“We went from having no answer when auditors asked how we govern AI to having a framework we can actually defend – and the guardrails give our risk team confidence we hadn’t had before.”
– CISO or Head of AI Governance.
A North American technology organisation deploying AI across five business units without guardrails, governance, or audit infrastructure was transformed through Ksolves AI/ML consulting services. A production-grade AI Security framework now enforces prompt-level filtering, role-based access, continuous dependency scanning, and ISO 42001-aligned governance. 100% of injection patterns blocked. 40+ dependencies cleared. First ISO 42001 evidence pack produced. Zero cross-role data exposure. A repeatable governance model extendable to new integrations without rebuilding controls – turning a compliance liability into a competitive differentiator.
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