Manager | Hybrid cloud | Bengaluru | Engineering | Hybrid Cloud Engineering
• Job requisition ID : 107229
• Location: Bengaluru
• Entity: Deloitte Touche Tohmatsu India LLP
Job Title: Manager – Security & Compliance Architect (AI Infrastructure)
Role Overview
We are seeking a Manager-level Security & Compliance Architect to design and implement secure, compliant, and resilient AI infrastructure platforms, including GenAI, ML pipelines, and data ecosystems.
This role will focus on embedding security-by-design and compliance-by-default principles across AI systems, ensuring protection of data, models, and infrastructure while aligning with regulatory and industry standards.
Key Responsibilities
1. AI Security Architecture
- Design and implement end-to-end security architecture for AI/ML and GenAI platforms:
- Model training and inference environments
- LLM and API integrations
- Data pipelines, vector databases, and orchestration frameworks
- Define secure reference architectures for:
- Cloud-native AI platforms (Azure, AWS, GCP)
- Hybrid and multi-cloud deployments
- Implement defense-in-depth strategies across AI systems
2. AI-Specific Threat Modeling & Risk Management
- Conduct threat modeling for AI systems covering:
- Model poisoning
- Prompt injection and jailbreaking
- Data leakage and inference attacks
- Identify and mitigate AI-specific vulnerabilities across:
- Training data pipelines
- Model artifacts and endpoints
- Perform risk assessments and define mitigation strategies aligned to enterprise risk appetite
3. Compliance & Governance
- Ensure AI platforms adhere to global and regional standards such as:
- ISO 27001, SOC 2, NIST, CIS benchmarks
- GDPR, HIPAA (as applicable)
- Emerging AI regulations (e.g., EU AI Act, responsible AI guidelines)
- Define and implement:
- Data governance and privacy frameworks
- Model governance and lifecycle controls
- Support audit readiness, compliance reporting, and certifications
4. Identity, Access & Data Security
- Define and implement:
- Zero Trust architecture for AI platforms
- Fine-grained access controls (RBAC/ABAC)
- Secure:
- Training and inference data
- Model endpoints and APIs
- Secrets, tokens, and embeddings
- Implement encryption strategies:
- Data at rest and in transit
- Secure key management (HSM, KMS)
5. Secure AI Development & MLOps
- Embed security into:
- CI/CD and MLOps pipelines
- Model development and deployment lifecycle
- Implement:
- Secure coding and model development best practices
- Dependency and artifact security (SBOMs, vulnerability scanning)
- Establish controls for:
- Model versioning and integrity
- Supply chain security
6. Monitoring, Detection & Incident Response
- Design security monitoring for AI platforms:
- Anomalies in model outputs
- Data exfiltration attempts
- Unauthorized access patterns
- Integrate with enterprise:
- SIEM / SOAR platforms
- Threat intelligence systems
- Define incident response plans for AI-specific risks
- Conduct security drills and simulations
7. Tooling & Platform Enablement
- Implement and manage security tools such as:
- Cloud-native security (Defender, GuardDuty, Security Command Center)
- Container security (Aqua, Prisma, etc.)
- API security & gateways
- Evaluate and integrate AI security tools (prompt filtering, model monitoring, adversarial testing)
- Build automated guardrails using policy-as-code
8. Stakeholder Engagement
- Work with:
- AI/ML engineering teams
- Data science and platform teams
- Enterprise security and compliance groups
- Translate technical risks into business impact and compliance needs
- Support leadership with:
- Security posture reporting
- Risk dashboards and remediation plans
Required Qualifications
Experience
- 8–12 years of experience in:
- Cybersecurity architecture / cloud security
- Compliance and risk management
- 3–5+ years in cloud-native or AI/ML environments
- Hands-on experience in designing secure distributed systems
Core Skills
- Deep understanding of:
- Security architecture principles (Zero Trust, defense-in-depth)
- Cloud security frameworks and controls
- Compliance standards and regulatory frameworks
- Strong knowledge of:
- AI/ML lifecycle and associated risks
- Data security and privacy engineering
Technical Skills
- Cloud Platforms: Azure, AWS, GCP
- Security:
- IAM, encryption, network security, secrets management
- AI/ML:
- LLM APIs, model pipelines, data pipelines
- DevSecOps:
- CI/CD security, SAST/DAST, container security
- Tools:
- SIEM (Splunk, Sentinel), vulnerability management, API security
Leadership & Consulting Skills
- Strong stakeholder management and communication skills
- Ability to translate security into business and compliance outcomes
- Experience working in cross-functional teams and transformation programs
Preferred Qualifications
- Certifications:
- CISSP, CISM, CCSP
- Azure Security Engineer / AWS Security Specialty
- Exposure to:
- Responsible AI frameworks
- Privacy-enhancing technologies (PETs)
- Experience in:
- Multi-cloud and regulated environments (BFSI, healthcare, etc.)