Lead AI Security Architect

Responsibilities

• Collaborate with Group Security and Data Governance teams to align AI platform designs with enterprise security and compliance policies.

• Define and implement secure-by-design architectural principles for AI/ML platforms, covering data pipelines, model deployment, and access layers.

• Implement and oversee Responsible AI (RAI) practices to ensure AI systems are designed and deployed ethically, with fairness, transparency, and compliance

• Implement and oversee Explainable AI (XAI) practices to ensure AI model decisions are transparent, interpretable, and trustworthy through integrated explainability features.

• Ensure compliance with regulatory frameworks and AI governance standards.

• Ensure secure and compliant architecture in collaboration with cybersecurity and governance teams, embedding PDPA and enterprise policy requirements into designs

• Translate governance requirements into technical specifications and enforceable controls across cloud and on-premise AI environments.

• Integrate privacy-preserving mechanisms such as data anonymization, encryption, tokenization, and secure logging into AI workflows.

• Evaluate and recommend AI security and governance tools (e.g. AWS Guardrails, Azure Responsible AI, IBM Watson Governance) for adoption.

• Conduct AI-specific risk assessments, including model misuse, bias, data leakage, adversarial attacks, and LLM prompt vulnerabilities.

• Review and approve the integration of third-party AI services and open-source models from a security and compliance perspective.

• Champion awareness of AI security and governance across AIDA by contributing to policies, best practices, and team enablement sessions.

• Review and clear governance approvals related to architecture and solution design, with specific focus on AI security.

• Collaborate with vendors and partners to review, evaluate, and select appropriate security solutions.

• Ensure all AI solutions, including in-house and vendor-developed systems, undergo thorough testing and Vulnerability Assessment and Penetration Testing (VAPT) to safeguard security and reliability.

Requirements

• Bachelor's or Master's degree in Cybersecurity, Engineering, AI/ML, or related field.

• More than 5 years of experience in cybersecurity, data governance, or secure systems architecture, with at least 3 years focused on AI or cloud-based ML systems

• Proven expertise in implementing cybersecurity governance, data protection, etc on data or AI platform.

• Strong understanding of AI/ML pipeline components and risks—model misuse, prompt injection, data leakage, adversarial inputs, bias and explainability.

• Proficient in implementing secure and compliant AI/ML systems on cloud platforms such as AWS SageMaker, Azure ML, Google Vertex AI, etc.

• Experience with AIOps/LLMOps and DevSecOps practices, including secure CI/CD, RBAC, secrets management and logging

• Familiarity with AI governance toolkits and regulatory trends

• Technical knowledge of data privacy controls (encryption, tokenization, data minimization) and security frameworks (e.g., Zero Trust, OWASP for ML)

• Ability to perform threat modeling and security assessment for AI and LLM-based systems

• Strong cross-functional communication and collaboration skills, with the ability to influence both technical and policy-level decisions

• Good internal (IT, Networks, business) and external (suppliers, government) stakeholders management skills

• Strong technical writing and presentation skills, with the ability to communicate complex concepts clearly to both technical and non-technical stakeholders.

• Proactive and fast learner with a strong drive to stay current on emerging technologies and industry trends

• Proven experience working in a telco environment or implementing security and governance role is a plus

Licence no: 12C6060