Domain Manager - Cyber Risk Unit

Job description

Job Summary • Identify and exploit vulnerabilities in AI models and systems through realistic adversarial simulations to prevent exploitation by malicious actors. • Evaluate models for security weaknesses and risks like supply chain, AIBOM vulnerabilities across GenAI Agentic platform. • Hands on experience on critical attack techniques including prompt injection, jailbreaking, data poisoning, model inversion, and good understanding on OWASP LLM Top 10, OWASP Agentic Top 10, MITRE ATLAS framework. • Plan and conduct targeted red team engagements across large language models, generative AI applications, and supporting ML infrastructure. • Produce detailed, actionable reports with clear attack reproductions, impact assessments, and recommended hardening measures. • Good understanding on Guardrail implementation, Rule engine, Policy configuration and AI Runtime security. • Safeguard live AI inference environments and deployed models against active runtime threats including model extraction, evasion, backdoor activation, and prompt-based attacks. • Implement layered runtime defenses such as input/output sanitization, anomaly detection, rate limiting, and content scanning tailored to AI workloads. • Perform threat modeling and risk analysis tailored to agentic architectures, focusing on risks such as goal misalignment, tool misuse, and privilege escalation. • Work closely with MLOps, platform, and SRE teams to embed runtime protection practices into deployment pipelines and operational processes. • Lead incident response for AI-specific security events, performing root-cause analysis, containment, and remediation with minimal service disruption. • Assess Third Party Partner vulnerabilities and security risk • Create and maintain specialized tooling, test harnesses, and automation to enable efficient, repeatable adversarial security testing for AI systems. • Regular cadence with AI Development team for secure design, suggest architectural reviews. • Connect with AI Duties and Responsibilities Minimum Accountabilities • Engineering / Computer Graduate with 4-6 years of Information / Cyber Security Experience and minimum 1-2 years of hands-on experience on AI Red Teaming, AI Guardrails implementation will be preferred. Additional Accountabilities • AI securityrelated certifications (Good to Have) Key Decisions / Dimensions • Involvement in Activity calendar planning and execution of AI applications • Preparation of testcases for AI Red Teaming and highlight the risk imposing of the vulnerabilities Major Challenges • AI Red Teaming understanding, execution, reporting timely closure from stakeholders • Change management and AI sign off with in defined TAT Required Qualifications and Experience Qualifications • Post-Graduates with relevant security experience of 4-6years (also graduates with experience of 6-8 years may apply) Work Experience • Engineering / Computer Graduate with 4-6 years of Information / Cyber Security Experience • Understanding of OWASP LLM Top 10, OWASP Agentic Top 10, MITRE ATLAS framework. • Common AI-specific threats: Prompt injection (direct indirect), Model poisoning data poisoning, Model theft / extraction, Hallucination abuse unsafe outputs • Threat modeling for AI systems (LLMs, RAG, agents) • Strong understanding of application security (OWASP Top 10, API Security Top 10) • Secure SDLC practices (design reviews, threat modelling, secure coding)