Role Purpose

The Data Platform & AIAccess ControlLeadisresponsibleforensuringthe secure, compliant, and responsible usage of organizational data and AI systems. They bridge the gap between AI engineering, information security, and governance by defining permissions for both human users, Functional Accounts, Service Principlesand autonomous AI agents acrossmodern Data Platforms, Analytics ecosystems, AI/ML solutions, and Generative AI environments.

This role will be responsible for establishing secure and compliant Data & AIAccessecosystems by integratingIAM, governance, privacy, risk management, and regulatory compliance intoData Platforms and AI Operations

The ideal candidate will possess deep expertise inAccess Management practices incloud-native data,AIplatforms, AI/GenAI security,Role Based Access Control methodologies, regulatory compliance frameworks, and secure engineering practices.

The role requires strong collaboration acrossData &AIPlatform Engineering, IT, Security, Legal,and AI Engineering teams(data science/DevOpsMachine Learning Engineering)to ensuresecure, compliant and resilientaccess control management across Data & AIenvironments

Key responsibilities:

Data & AI PlatformsIdentity & Access Management Governance

• Entity Permissions:Define and manage access rights for both human employees and non-human entities (e.g., AI agents,MLOpspipelines, service accounts).

• Access Control Matrix:Develop and maintain Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) matrices to dictate who (or what) can read, write, modify, or execute datasets and models.

• Principle of Least Privilege:Enforce minimum-necessary access to limit the blast radius ofdataexposure and internal threats

• Access Architecture:Design end-to-end authorization frameworks enforcing least-privilege and Attribute-Based Access Control (ABAC) across Azure data estates

• IDAM ToolsIntegration:Manage integrations betweenIDAM Tools like Microsoft Entra ID(Azure AD),SailPoint,Octaetcand data platforms (Azure Data Lakes,Azure Synapse, Fabric, and Databricks, and AI/ML Platforms).

• Lifecycle Management:Automate access provisioning, role mapping, and de-provisioning workflows for clinical, research, and business users.

• Define, monitor & trackAccessControls, risk metrics & KPIs against Data Access Management Policies

• Identify and mitigate Data & AI-Specific access risks including Data Leakage, Unauthorized Data Exposure, etc.

• Maintain enterprise Access Management policies, standards, and control frameworks

• EnsureSecureaccess toenterprise data ecosystems including but not limited to: Data Lakes, Data Warehouses, ETL/ELT Pipelines, Analytics Platforms, Data APIs, Gen AI Applications and Services.

• Ensure implementation of secure data access, encryption, masking, retention, and governance controls across Data Engineering & MLOps pipelines.

• Conduct Data Access(IDAM)Compliance& Data Security Assessment:Microsoft Fabric, Azure Data Services, Databricks, Snowflake, enterprise Data & AI platforms and Applications by conductingMonthly, Quarterly & Half-Yearly and Yearly Access assessment Audits

• Maintain SOPs, Runbooks and Knowledge Repositories.

• Maintain risk registers andaccessgovernance reporting mechanisms.

• Support investigations andPerform root cause analysisrelated to Data & AI platformaccessviolationincidents

• Collaborate withBusiness users, Application Owners,IAM teams to ensure enterprise IDAM policies implemented across Data & AI platforms.

• Closely working with AI Governance & Responsible AIcentral teams to align with enterprise policies and regulatory expectations.

• Implement automated compliance reporting and logging to audit how Data & AI models interact with sensitive corporate and customer data.

• Lead ComplianceInitiatives aligned with ISO 27001, SOC2, GDPR, PCI-DSS, HIPPA,DPDP and AI Governance Frameworks.

• Coordinate audits, assessments, and evidence management activities.

• Partner with Legal, Privacy, Risk, and Audit teams to ensure regulatory adherence.

• Contribute directlytohands‑on capacitytoGen AI Operationstroubleshooting, and optimisation.

Leadership& Stakeholder Engagement

• Cross-Functional Liaison:Serve as the primary connector bridging Data Science/AI Engineering teams with Legal, Privacy, and Cybersecurity departments

• Strong collaboration with Data & AI Operations Lead, Data & AI Security Compliance lead to ensure alignment among all services towers

• MentorData Engineering, Access and Operationsprofessionals.

• Lead awareness and training programs related toData & AI Access Managementbest practices.

• Escalate and remove blockers in a complex, matrix organisation, engaging senior stakeholders as required.

• Act as the primary point of contact forData & AIPlatform AccessManagement Programs.

• Drive user adoption, service awareness, training coordination, and feedback management.

• Ensure TimelyCommunication to stakeholders duringAccess Control Shortfalls,High Impact Incidents and Changes.

• SupportAI Governance and Responsible AIprocesses through engagement with Architecture Review Boards andAI/MLArchitects

• Ensuring close alignment to internal & external regulatory and compliance requirements,AccessGovernance& Data Lifecycle Auditswith no critical or high findings or violation against them.

Key Skills

• Identity & Access Management

• AI & GenAIDataSecurity

• AccessCompliance & Governance

• Data Platform& CloudSecurity

• AccessRisk Management

• Regulatory Compliance

• Security, Risk and Compliance Audit Management

• Stakeholder Management

Qualifications&Experience

• Bachelor’s ormaster’sdegree in Cybersecurity, Computer Science, Information Systems or related field.

• 10+ Years of experience in:

• Identity & Access Management

• Enterprise Data Security

• Compliance and Governance

• Proven track record of enterpriseIAM and IDAM policies.

• Strong understanding of SSO, Tokenized Access, PAM (privileged access Management)

• Knowledgeof at leastoneIDAM / IAM tool wiz Okta, Sailpoint, Cyberarc, Microsoft Entra ID etc

• Strong experience in representing the organization to Internal and ExternalAccessManagement, Risk and Compliance Audits.

• Strong Experience with Enterprise Data, AIand Analytics Ecosystem

• Hands On Experience withDataPlatforms,AI/ML or Generative AI Security.

• Knowledge of DevSecOps, MLOps, CI/CD, API security, and container security.

• Deep understanding of cloud platforms (Azure, AWS, Google Cloud)

• Strong strategic and operational leadership skills.

• Excellent communication and executive presentation abilities.

• Ability to influence global cross-functional teams.

• Strong analytical and problem-solving capabilities.

• Passion for emerging technologies, cybersecurity innovation, and responsible AI governance.