Associate Director - Cyber Security (AI & Data Science)
We are looking for a strategic and hands-on Cybersecurity AI leader to define and drive how the organization applies Artificial Intelligence, including Agentic AI, to cybersecurity. The mandate is to make the security function materially faster, more accurate, and less manual by putting AI into the work it does every day - and to do that responsibly, with the controls and governance that AI-driven security decisions require.
The role will lead the development of AI-enabled cybersecurity capabilities, including AI-driven SOC, security automation, threat detection, identity and access intelligence, data security, insider-threat detection, third-party risk, vulnerability management, security engineering, and cyber risk analytics.
This is a build-and-prove role. Much of the commercial tooling in this space is immature, and you will be expected to tell the difference between what works and what demos well.
Key Responsibilities and Duties
AI for Cybersecurity
Lead the use of AI to transform how the security function operates, focused on operational efficiency, risk reduction, and improved customer experience:
- AI-driven detection, triage, and investigation in the SOC
- Autonomous and semi-autonomous response, and the control model that makes it safe
- Vulnerability identification, prioritization, and remediation
- Identity, access, and Insider threat analytics
- Measurable reduction in manual security operations toil
Cybersecurity AI Strategy
- Define and execute the enterprise Cybersecurity AI strategy and roadmap.
- Identify high-value cybersecurity use cases for GenAI, ML and Agentic AI.
- Build business cases, investment roadmaps and measurable ROI for AI-driven security transformation.
- Establish responsible and secure adoption of AI across Cybersecurity.
AI Governance & Risk
- Establish AI security standards, policies and control frameworks.
- Partner with Legal, Privacy, Risk, Compliance, Data and Enterprise Architecture teams.
- Map controls to relevant regulatory and industry frameworks.
- Define AI security metrics, KRIs and executive dashboards.
Engineering and Delivery
- Lead architecture and delivery of AI-enabled security capabilities, including integration into existing SOC, SIEM, SOAR, IAM, DLP, EDR, CSPM, and GRC ecosystems
- Build in-house where commercial solutions are insufficient
- Establish the data foundations these capabilities depend on — telemetry quality, labeling, feature pipelines, and feedback loops from analyst decisions back into models
Leadership & Stakeholder Management
- Lead and grow a team of cybersecurity, security engineering, and data scientists.
- Partner with CISO, CIO, CTO, Chief Data Officer, Chief AI Officer and business leadership.
- Present AI security strategy, risk and investment requirements to senior leadership.
- Develop strategic partnerships with technology vendors, startups and research organizations.
- Mentor and develop senior cybersecurity and AI talent.
Educational Requirements
- University (Degree) Preferred
Work Experience
- 5+ Years Required; 7+ Years Preferred
Physical Requirements
- Physical Requirements: Sedentary Work
Career Level
9PL
Job Description — Associate Director / Head of Cybersecurity AI
Position
Associate Director – Cybersecurity AI / Head of Cybersecurity AI
Experience
12–15+ years in Cybersecurity, Information Security, AI Security, Security Engineering, or related technology leadership roles.
Role Overview
We are looking for a strategic and hands-on Cybersecurity AI leader to define and drive the organization’s strategy for applying Artificial Intelligence and Agentic AI to cybersecurity, while establishing security, governance, and risk controls for AI adoption across the enterprise.
The role will lead the development of AI-enabled cybersecurity capabilities, including AI-driven SOC, security automation, threat detection, identity and access intelligence, data security, insider-threat detection, third-party risk, vulnerability management, security engineering, and cyber risk analytics.
The leader will also establish a Zero Trust–aligned security framework for AI and autonomous agents, covering non-human identities, intent, access, data usage, model interactions, tool invocation, and autonomous actions.
Key Responsibilities
1. Cybersecurity AI Strategy
- Define and execute the enterprise Cybersecurity AI strategy and roadmap.
- Identify high-value cybersecurity use cases for GenAI, ML and Agentic AI.
- Build business cases, investment roadmaps and measurable ROI for AI-driven security transformation.
- Establish responsible and secure adoption of AI across Cybersecurity.
2. AI for Cybersecurity
Lead AI transformation across
- AI-enabled SOC and Security Operations
- Threat detection and investigation
- Incident response automation
- Security orchestration and autonomous response
- Vulnerability and exposure management
- Identity and access intelligence
- Insider-threat detection
- Data Loss Prevention and data classification
- Third-party / supply-chain cyber risk
- Threat intelligence
- Security analytics and cyber-risk dashboards
- Application and cloud security
- Security engineering automation
3. Security of AI / AI Security
Define controls and architecture for securing enterprise AI environments, including:
- GenAI and LLM security
- AI applications and copilots
- Agentic AI
- AI agents and non-human identities
- Model and API security
- Prompt injection and data leakage
- AI supply-chain/model risks
- RAG and vector database security
- AI data governance and lineage
- Model access and authorization
- AI red teaming and adversarial testing
- AI runtime monitoring
4. Agentic AI & Zero Trust
Establish a Zero Trust security model for autonomous AI agents, covering:
Identity → Intent → Access → Data → Action → Continuous Validation
Key areas include
- Non-human identity management
- Agent authentication and authorization
- Role/attribute-based access control
- Just-in-time and least-privilege access
- Agent-to-agent communication
- Tool/API access governance
- Data access and classification
- Autonomous action controls
- Human-in-the-loop controls
- Agent activity monitoring and auditability
- Dynamic risk-based policy enforcement
- Blast-radius containment
5. AI Governance & Risk
- Establish AI security standards, policies and control frameworks.
- Partner with Legal, Privacy, Risk, Compliance, Data and Enterprise Architecture teams.
- Develop AI risk assessment and security review processes.
- Map controls to relevant regulatory and industry frameworks.
- Define AI security metrics, KRIs and executive dashboards.
6. Cybersecurity Engineering & Transformation
- Lead architecture and implementation of AI-enabled security platforms.
- Integrate AI capabilities across existing SOC, SIEM, SOAR, IAM, DLP, EDR, CSPM and GRC ecosystems.
- Drive automation and reduction of manual security operations.
- Evaluate emerging cybersecurity AI technologies, startups and platforms.
- Build internal AI security capabilities where commercial solutions are insufficient.
7. Leadership & Stakeholder Management
- Lead a team of cybersecurity, AI security, security engineering and data/AI specialists.
- Partner with CISO, CIO, CTO, Chief Data Officer, Chief AI Officer and business leadership.
- Present AI security strategy, risk and investment requirements to senior leadership.
- Develop strategic partnerships with technology vendors, startups and research organizations.
- Mentor and develop senior cybersecurity and AI talent.
Required Experience & Skills
Core Cybersecurity
- 12–15+ years of cybersecurity / information security experience.
- Strong understanding of security architecture, SOC, IAM, AppSec, Cloud Security, GRC and security engineering.
- Experience leading enterprise-scale cybersecurity transformation.
- Strong understanding of Zero Trust principles.
AI / ML
- Strong understanding of:
- Generative AI
- LLMs
- Machine Learning
- RAG
- AI agents
- Agentic workflows
- AI APIs and orchestration
- AI security architecture
- Experience implementing AI/ML solutions in cybersecurity.
- Understanding of AI risks, model security and AI governance.
Technical Leadership
- Ability to translate emerging AI technologies into production-grade cybersecurity capabilities.
- Experience with cloud platforms and modern security architectures.
- Understanding of APIs, microservices, containers and DevSecOps.
- Experience integrating AI with enterprise security platforms.
Governance & Risk
Knowledge of relevant frameworks such as
- NIST Cybersecurity Framework
- NIST AI RMF
- Zero Trust Architecture
- ISO 27001
- SOC 2
- GDPR / CCPA
- PCI DSS
- SOX / ITGC
- Industry-specific cybersecurity regulations
Preferred Qualifications
- Master’s degree in Cybersecurity, Computer Science, AI, Technology or related discipline.
- CISSP / CISM / CCSP or equivalent certification.
- Experience building cybersecurity AI capabilities from the ground up.
- Experience with AI agents / autonomous security operations.
- Experience in financial services, technology, fintech or other highly regulated industries.
- Published research, patents, conference presentations or thought leadership in Cybersecurity/AI is a plus.
- Experience working with cybersecurity startups and emerging technologies is desirable.
Related Skills
Collaboration, Continuous Improvement Mindset, Data-Driven Business Intelligence, Data Engineering/Analytics, Data Visualization, Predictive Modeling, Problem Solving, Programming, Resourcefulness, Statistics, Story Telling
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Company Overview
TIAA Global Capabilities was established in 2016 with a mission to tap into a vast pool of talent, reduce risk by insourcing key platforms and processes, as well as contribute to innovation with a focus on enhancing our technology stack. TIAA...