Client Profile
The client is a global Software-as-a-Service (SaaS) provider specializing in AI-driven customer experience platforms. With over 5,000 enterprise clients across 30+ countries and a development team of 800+ engineers, the client integrates AI into all facets of their operations – from automated chatbots to decision-making engines. As the adoption of AI grew, so did the associated risks. Concerns around adversarial ML, model poisoning, and data governance triggered the need for a comprehensive AI security posture assessment.
Challenges Faced
Key security concerns included:
- Lack of visibility into AI model risk and governance
- Potential vulnerabilities in model training pipelines
- Data integrity concerns in training datasets
- Absence of AI-specific security policies and controls
Solution
COE Security implemented a tailored AI Security Posture Assessment Engagement, combining:
- Threat Modeling for AI Pipelines: Identified attack surfaces across ML lifecycle
- Data Supply Chain Audit: Verified dataset provenance and manipulation risks
- Model Risk Evaluation: Tested models against adversarial and evasion techniques
- Governance Framework Mapping: Designed controls aligned with NIST AI RMF & ISO/IEC 23894
AI Risk Discovery and Mitigation Actions
- Mapped AI/ML architecture and workflows from data ingestion to model deployment
- Conducted red team simulations targeting ML attack vectors (e.g., model inversion, poisoning)
- Discovered data drift and implemented monitoring with alert thresholds
- Reviewed open-source dependencies in AI toolkits (TensorFlow, PyTorch, etc.)
- Built risk registers and proposed mitigation measures for all identified gaps
Governance, Controls, and Strategic Recommendations
- Established an AI governance committee and policy charter
- Integrated secure model lifecycle checkpoints in CI/CD
- Developed explainability and bias mitigation controls
- Defined risk rating and audit tracking methodology for AI components
COE AI Security Posture Assessment Service Portfolio
- AI/ML Risk Assessments
- Threat Modeling for AI Pipelines
- Adversarial Testing & Model Hardening
- Data Supply Chain Integrity Audits
- AI Governance Frameworks (ISO 23894, NIST AI RMF)
- Secure MLOps Enablement
- Bias & Fairness Testing
- Explainability Validation (XAI)
- AI Audit Trail Automation
- Developer Training in AI Secure Engineering
Implementation Details
- Deployed model evaluation sandbox and adversarial testing tools
- Integrated model integrity scans into existing CI/CD pipeline
- Delivered tailored training sessions to 60+ ML engineers and data scientists
- Produced detailed AI threat models and process documentation
- Set up quarterly AI security reporting aligned with board KPIs
Results Achieved
- 70% improvement in AI model governance maturity (baseline to benchmark)
- Reduced AI model attack surface by 60% via patching and architectural hardening
- Established AI audit trails covering 100% of core ML workflows
- Increased stakeholder trust and regulatory alignment with ISO 23894 draft
Client Testimonial
“COE Security gave us clarity on AI risk where we had none. Their depth in both cybersecurity and machine learning helped us future-proof our models and meet emerging governance expectations.”