Senior AI Security Architect Consultant, NTT DATA

• The ideal candidate will combine deep cybersecurity expertise with hands-on knowledge of AI/ML technologies, ensuring that AI systems are resilient against adversarial threats, data leakage, and misuse
• 20+ years of experience in cybersecurity, with at least 3+ years in AI/ML security or data security
• Proven experience designing secure architectures for AI/ML systems
• Strong knowledge of:
• Machine learning frameworks (TensorFlow, PyTorch, etc.)
• Cloud platforms (Azure, AWS, GCP) and AI services
• Identity & access management, encryption, and network security
• Experience in threat modeling and risk assessment methodologies
• Bachelor’s or Master’s degree in Computer Science, Cybersecurity, AI/ML, or related field
• AI/ML security and adversarial techniques
• Cloud security architecture
• Data privacy and protection
• Threat modeling and risk analysis
• DevSecOps / MLSecOps
• API and application security
• Governance, risk, and compliance (GRC)
• Strong communication and stakeholder management

Desirable

• We are seeking an experienced Senior AI Security Architect Consultant to design, implement, and govern secure AI/ML systems across the enterprise
• This role is responsible for embedding security, privacy, and trust into AI solutions—from model development and deployment to monitoring and lifecycle management
• Define and lead the AI security architecture roadmap aligned to enterprise security strategy
• Develop secure-by-design frameworks for AI/ML pipelines, including data ingestion, training, inference, and deployment
• Establish AI trust, risk, and compliance controls (e.g., explainability, fairness, robustness)
• Conduct threat modelling for AI systems, identifying vulnerabilities such as:
• Adversarial attacks (evasion, poisoning)
• Model inversion and extraction
• Data leakage and privacy risks
• Define and implement risk mitigation strategies and controls
• Perform AI security risk assessments and integrate findings into governance processes
• Integrate security into ML pipelines (MLSecOps) including CI/CD and MLOps frameworks
• Define controls for:
• Secure dataset handling and lineage
• Model versioning and integrity validation
• Access control and secrets management
• Embed automated security testing into model development pipelines
• Ensure compliance with data protection regulations (e.g., GDPR, HIPAA where applicable)
• Implement privacy-preserving techniques such as:
• Differential privacy
• Federated learning
• Data anonymization and synthetic data
• Define policies for sensitive data usage in AI models
• Design safeguards for:
• Large Language Models (LLMs) and generative AI (prompt injection, hallucinations, data exfiltration)
• API and model endpoint security
• Implement guardrails and monitoring solutions for generative AI usage
• Establish AI security standards, policies, and guidelines aligned to frameworks such as:
• NIST AI Risk Management Framework
• ISO/IEC 27001, 23894
• Support regulatory compliance and audits related to AI security
• Partner with data scientists, ML engineers, DevOps, and security teams to embed security practices
• Act as a trusted advisor to business and technology stakeholders on AI-related risks
• Provide security design reviews for AI initiatives
• Define monitoring for model drift, anomalies, and misuse detection
• Develop playbooks for AI-related security incidents, including model compromise or data breaches
• Lead investigations involving AI system risks