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