Capability Leader AI Security

NTT DATA, Inc.; Hyderabad, Bengaluru; Hybrid; INR 6,000,000 - 9,000,000; Full time

Job summary

NTT DATA, Inc. in Hyderabad seeks a Capability Leader AI Security with deep expertise in securing AI/ML systems and leading security practices across the lifecycle. You will mentor engineers, shape reference architectures, and drive secure-by-design patterns.

Responsibilities include governance, threat modeling, compliance alignment, and collaboration with data science, product, and platform teams to advance AI security maturity.

Qualifications

• 15+ years in application security with 8+ years in leadership roles.
• Proven track record building and managing security practices.
• Strong knowledge of penetration testing, red/purple teaming, and breach simulation.
• Experience with practice development, go-to-market strategies, and sales enablement.
• Certifications such as OSCP, CEH, CISSP, CISA, or CISM.

Responsibilities

• Define security architecture for AI/ML systems and mentor engineers.
• Drive secure-by-design patterns across the AI/ML lifecycle.
• Lead platform security across AI Platform Admin, Reusable Utilities, and Infrastructure.
• Develop governance, threat modeling, and compliance mappings to NIST/OWASP.
• Create runbooks and reference implementations to raise engineering quality.

Skills

Cybersecurity architecture

Leadership

Communication

Threat modeling

Education

Security certifications: OSCP, CEH, CISSP, CISA, CISM

Tools

LangChain

LangGraph

AutoGen

MLOps platforms

Job description

Job Title: Capability Leader AI Security

Job Summary

AI Security Solution design leader should possess strong experience in AI Security and AI technologies and trends. He/she should actively contribute to the development and maturity of the AI security practice. This includes hands-on expertise AI security, supporting practice growth, enabling go-to-market initiatives, and contributing to thought leadership and best practices within the practice.

Ideal Qualifications

• Experience: 5+ years in cybersecurity architecture with proven experience securing large-scale LLM deployments and multi-agent workflows.
• Technical Proficiency: Hands-on capability with agent frameworks (e.g., LangChain, LangGraph, AutoGen) and MLOps platforms.
• Framework Knowledge: Deep familiarity with model risk management principles and AI security standards

Experience:

• 15+ years of experience in application security, with 8+ years in leadership roles
• Proven track record of building and managing successful security practices
• Strong knowledge of penetration testing, red teaming, purple teaming, breach attack simulation, and ethical hacking
• Experience with practice development, go-to-market strategies, and sales enablement
• Excellent leadership, communication, and interpersonal skills
• Industry-recognized security certifications (e.g., OSCP, CEH, CISSP, CISA, CISM)

Responsibilities:

We are seeking an experienced and highly skilled AI Security hands-on, highly technical architect responsible for defining security architecture and implementing robust security controls for our AI/ML systems and their underlying platforms and will serve as the teams technical mentor and architecture authority, driving secure-by-design patterns across the AI/ML lifecycle (data, training, evaluation, deployment, and production monitoring) and proactively mitigating AI-specific threats such as model integrity risks, data poisoning, adversarial attacks, prompt injection, model extraction, and inference-time abuse. Lead technically, set standards, and guide engineers day-to-day through architecture, reviews, and delivery.

Ensures AI systems are secure, compliant, and resilient by implementing data protection, threat detection, guardrails, and ongoing risk monitoring across the AI lifecycle.

Platform & Enablement Roles

• AI Platform Admin (M365, copilot Studio) Manages AI platforms and environments, including access provisioning, governance controls, and policy enforcement (e.g., DLP, security, and compliance).
• AI Reusable Utility Develops reusable components (e.g., prompts, connectors, APIs, templates) to accelerate AI solution delivery and promote standardization across use cases.
• AI Common Infrastructure, Framework & Observability Architect (AWS and Azure) Designs and maintains the foundational AI infrastructure, frameworks, and observability capabilities (telemetry, monitoring, metrics) required for scalable, reliable, and governed AI operations.

Core Responsibilities

• Agent Security
  Non-Human Identity & Access: Define strict Role-Based Access Control (RBAC) and least-privilege models for AI agents using identity systems (e.g., Entra Agent ID).
  Guardrails & Sandboxing: Design runtime environments with restricted permissions to prevent manipulated agents from accessing unauthorized APIs, data sources, or executing malicious toolchains.
  Input/Output Protection: Implement defenses against adversarial attacks, prompt injections, jailbreaking, and sensitive data leakage (DLP) across agent workflows.
• Observability & Monitoring
  Decision Traceability: Architect logging and monitoring standards to map how reasoning agents use data and call APIs, eliminating "black box" decisions.
  Model Drift & Integrity: Monitor models and prompt templates in production to detect behavioral drift, anomalies, and poisoning or evasion attacks.
• SOC Monitoring & Automation
  Autonomous Security (AI SOC): Design LLM-driven and agentic workflows to improve alert triage, contextual correlation, false-positive filtering, and playbook automation.
  Incident Response Playbooks: Establish remediation strategies and threat-hunting procedures for AI-specific events (e.g., compromised model artifacts, hallucination-driven exploits).
• Compliance Enablement & Governance
  Regulatory Alignment: Map AI-specific controls to established standards like the NIST AI RMF, OWASP Top 10 for LLMs, and GDPR.
  Audit Readiness: Build audit pipelines that track and explain everything an agent does to satisfy ongoing AI regulatory compliance and governance requirements.

Architecture & Secure-by-Design Leadership

• Define and maintain AI security reference architectures for multiple AI deployment patterns, including MCP / Agentic AI and LLM application stacks (RAG, tools/plugins, agents, orchestration).
• Establish and evolve security requirements, patterns, and guardrails across the AI/ML SDLC (design build run), including secure pipelines and platform controls.
• Create solutions for Security for AI solution leveraging prominent partner platforms.

Control Design & Implementation (Hands-on)

• Design and deploy controls to ensure model integrity and governance, including RBAC/ABAC for models, feature stores, data sets, registries, and evaluation artifacts.
• Build/enable technical mechanisms for provenance, attestation, signing, and approval workflows (where applicable) across datasets, models, prompts, and deployments.
• Design and Drive implementation of runtime protections for AI services (abuse prevention, rate limiting, input/output validation, prompt-injection mitigations, model endpoint hardening, and monitoring).

Threat Modeling, Assurance, and Risk Reduction

• Conduct and lead AI/ML-specific threat modeling (data poisoning, model evasion, extraction, inversion, supply-chain, prompt attacks), translate findings into actionable backlogs, and drive remediation.
• Define and run security design reviews for AI initiatives; provide clear, pragmatic architecture guidance and document exceptions with risk acceptance paths.
• Establish AI security testing approaches (adversarial testing, red-teaming enablement, evaluation security, misuse/abuse cases) and integrate into delivery pipelines.

Tooling, Automation, and Operational Enablement

• Define and design AI security tooling to improve and automate cybersecurity posture (e.g., controls coverage, policy-as-code, detection engineering, vulnerability management integration, incident response playbooks for AI-specific events).
• Define logging/monitoring standards and detection use-cases for AI platforms and LLM apps (drift signals, anomalous access, suspicious prompt patterns, exfiltration indicators, policy violations).

Technical Mentorship & Influence (No Line Management)

• Act as the teams technical mentor: coach engineers through designs, implementations, and trade-offs; raise engineering quality via reviews, pairing, and knowledge sharing.
• Lead by influence across Data Science, Engineering, Product, Platform, and Cybersecuritydriving alignment without formal authority.
• Create internal enablement materials: runbooks, architecture standards, reusable patterns, and reference implementations.