AI Application Security Lead

Role Summary We are seeking an experiencedAI Application Security Leadto drive the secure development, deployment, and management of AI and GenAI applications across our enterprise. This role will focus on architecting and implementing robust security controls for AI/ML solutions, performing risk assessments, and leading threat modeling and security testing for agentic AI protocols (such as A2A, A2P, MCP). The ideal candidate brings a strong background in application security, cloud security, and hands-on AI/ML security, with the ability to collaborate effectively with business, engineering, and compliance teams to deliver secure, compliant, and resilient AI systems. Key Responsibilities Develop, implement, and continuously improve comprehensive AI/ML and GenAI security strategies, standards, and best practices for application and data security. Lead the creation and maintenance of security control frameworks and reference architectures for agentic AI applications, including protocols like A2A (Agent-to-Agent), A2P (Agent-to-Process), and MCP (Multi-Agent Control Plane). Collaborate with development and business teams to identify security requirements and embed security controls throughout the SDLC for AI/ML and GenAI applications. Conduct threat modeling and risk assessments using industry-standard methodologies (e.g., STRIDE, DREAD) for AI systems, APIs, and cloud-native services. Design and execute security testing strategies, including vulnerability assessments, penetration testing, and adversarial prompt testing for AI models and agentic protocols. Lead security architecture reviews for AI applications deployed across cloud platforms (AWS, Azure, GCP, OCI) and on-prem environments. Develop configuration hardening guidelines for cloud-native AI/ML services (e.g., AWS SageMaker, Bedrock, Azure Cognitive Services, GCP Vertex AI). Ensure compliance with relevant regulations and standards (e.g., SOC 2, ISO 27001, NIST, GDPR) and collaborate with legal and compliance teams to align AI systems with industry requirements. Drive business stakeholder communications: clearly articulate AI security risks, solutions, and value trade-offs to both technical and non-technical audiences. Present security metrics, dashboards, and reports on AI application security posture, incidents, and improvements to management and business stakeholders. Stay abreast of emerging threats, vulnerabilities, and innovations in AI/ML security, proactively recommending enhancements to security posture. Mentor and enable engineering teams on secure coding, prompt engineering, and AI application security best practices. Qualifications 8+ years of experience in Information Security, with at least 3+ years in secure SDLC, application security, or software engineering roles. Minimum 1+ year of hands-on experience in AI/ML or LLMOps security. Strong understanding of cloud security across AWS, Azure, GCP, OCI, and experience with native AI/ML services. Proficiency in application and data security, GenAI, LLMs, and prompt engineering; familiarity with OWASP LLM Top 10. Demonstrated experience with agentic AI protocols (A2A, A2P, MCP) and security architecture for distributed AI systems. Experience developing and executing security testing strategies, including penetration testing and adversarial testing for AI/ML applications. Threat modeling expertise using STRIDE, DREAD, or similar methodologies. Ability to develop reference security architectures and design patterns for proactive and automated controls. Strong knowledge of information security standards, data confidentiality, and regulatory frameworks. Excellent communication skills, with the ability to influence and present security metrics to business stakeholders. Strong analytical, problem-solving, and organizational skills, with experience managing multiple priorities in a global environment. Preferred Certifications Security certifications such as CISSP, CCSP, CISM, OSCP Cloud certifications (AWS/Azure/GCP/OCI security specialties) Kubernetes certification (CKA) AI/ML certifications (e.g., AWS ML Specialty, Azure AI Engineer)

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