Client Profile
The client is a mid-sized, cloud-native SaaS enterprise delivering AI-driven analytics platforms to financial institutions. With a global customer base spanning 12 countries and a development workforce of 400+ engineers, the organization relies heavily on real-time AI decision engines embedded in its core services. After experiencing anomalies and unexplained behavioral shifts in production AI models, the client approached COE Security to evaluate and strengthen their runtime AI defense capabilities.
Challenges Faced
Key security concerns included:
- Undetected adversarial inference attacks at runtime
- Lack of monitoring for abnormal AI behavior or drift
- Vulnerabilities in AI microservices communicating over APIs
- No existing response playbook for AI-specific threats
Solution
COE Security implemented a tailored AI Runtime Defense Analysis Program, combining:
- Behavioral Baseline Modeling: Established expected behavior signatures for AI model outputs
- Runtime Threat Hunting: Deployed detection mechanisms for adversarial patterns and payloads
- Security Observability for AI Pipelines: Integrated telemetry for inference layer monitoring
- Incident Response Tuning for AI Events: Built AI-specific detection rules and response logic
Runtime Risk Identification and Remediation
- Monitored AI models in production across multiple services using model observability tools
- Detected multiple drift events and one targeted adversarial input incident
- Implemented dynamic input validation at API endpoints before model invocation
- Introduced anomaly alerts tied to model confidence score deviations
- Secured inter-service communication with encrypted AI payloads
Governance and Readiness for AI-Driven Threats
- Updated SOC playbooks to include AI threat classifications and response procedures
- Integrated runtime model insights into SIEM for continuous detection
- Designed dashboards for tracking AI model health, drift, and confidence integrity
- Recommended use of eBPF-based telemetry for AI inference nodes
COE AI Runtime Defense Analysis Service Portfolio
- AI/ML Runtime Defense Analysis
- Adversarial Input Detection Systems
- Real-time Drift Monitoring and Alerting
- Secure AI Model API Gateways
- AI-specific SOC Playbooks
- Secure AI Containerization (Kubernetes, Docker)
- AI Incident Forensics
- Production Model Health Dashboards
- Runtime Telemetry Injection
- Model Confidence Deviation Detection
Implementation Details
- Deployed telemetry agents on AI inference servers for runtime monitoring
- Integrated AI anomaly metrics into existing Prometheus-Grafana stack
- Conducted red-team simulation to test runtime defense effectiveness
- Delivered technical documentation with AI threat models and response guides
- Provided monthly executive reports with model drift and threat summaries
Results Achieved
- 100% runtime model visibility achieved across inference endpoints
- 3 critical AI vulnerabilities identified and remediated
- Enabled 24×7 AI threat monitoring integrated into SIEM
- SOC team’s AI incident response capability improved by 65% (based on drill performance)
Client Testimonial
“COE Security helped us gain real-time visibility into our AI systems and stop threats before they impacted customer trust. Their AI-aware approach to cybersecurity gave us a clear edge.”