Responsibilities
The Vice President, AI Security & Automation Enablement leads the Information Security capability responsible for secure artificial intelligence adoption, AI security, and reusable automation across W. R. Berkley Corporation. The role sets strategy, establishes priorities and investment plans, leads people and partners, and remains accountable for measurable security and business outcomes.
This executive builds organizational capability rather than serving as the primary developer. The VP translates emerging AI risk and opportunity into a governed portfolio, equips existing security teams to adopt AI and automation safely, and ensures that architecture, engineering, governance, operations, and business stakeholders have clear standards, reusable patterns, and accountable decision paths.
Primary Responsibilities
- Strategy, Portfolio and Investment Leadership
- Define and maintain the multi-year AI security and automation strategy, roadmap, operating model, and investment priorities for Information Security.
- Convert enterprise objectives and emerging risks into a prioritized portfolio with clear owners, funding needs, delivery milestones, operational acceptance criteria, and measurable outcomes.
- Advise the CISO and senior technology leadership on AI-related risk, capability gaps, investment tradeoffs, tool overlap, and opportunities to improve security delivery.
- People Leadership and Organizational Capability
- Lead and develop a multidisciplinary team spanning AI security, automation, architecture, engineering, security data, and related enablement capabilities.
- Establish clear accountability, role expectations, talent plans, succession coverage, performance objectives, and sustainable delivery practices.
- Build a force-multiplier model that enables existing Information Security teams to use approved AI and automation rather than creating a centralized team that becomes the sole builder for every use case.
- AI Security Governance and Enterprise Partnership
- Represent Information Security in enterprise AI governance and provide accountable security recommendations for AI use cases, platforms, vendors, and material changes.
- Align AI review activities with existing architecture, third-party risk, legal, procurement, privacy, and Building Permit processes to reduce duplication while preserving required oversight.
- Define escalation criteria for high-risk, restricted-data, autonomous, externally hosted, or materially changed AI use cases.
- Secure AI Adoption and Control Framework
- Establish and govern security standards, reference architectures, and control patterns for generative AI, machine learning, AI agents, model and data pipelines, and AI-enabled applications.
- Ensure appropriate controls for identity, access, data protection, secrets, logging, monitoring, human oversight, output validation, change control, resilience, and incident response.
- Maintain a risk-based approach that enables approved experimentation and production use while identifying and reducing unmanaged or unsanctioned AI use.
- AI and Automation Delivery Enablement
- Oversee delivery of reusable capabilities such as governed AI experimentation environments, security workflow automation, evidence generation, control validation, AI-assisted analysis, and security data integrations.
- Set technical direction and challenge designs while assigning detailed architecture, prototyping, coding, integration, testing, and operational runbook development to qualified team members and partners.
- Require production readiness, support ownership, measurable adoption, quality controls, rollback capability, and acceptance criteria before automated capabilities are operationalized.
- Visibility, Measurement and Risk Reduction
- Establish reliable visibility into AI systems, AI usage, agents, integrations, and material data flows within the scope of Information Security oversight.
- Define executive and operational measures for control coverage, review cycle time, automation reliability, adoption, realized capacity, risk reduction, and exceptions.
- Use trusted data to identify gaps, prioritize remediation, and demonstrate whether investments are producing the intended outcomes.
- Vendor, Partner and Financial Management
- Lead evaluation of AI security and automation vendors, strategic partners, and professional services based on risk, capability, interoperability, supportability, cost, and measurable value.
- Own portfolio-level commercial and sourcing recommendations, including build-versus-buy decisions, statements of work, acceptance of partner deliverables, and avoidance of unnecessary tool overlap.
- Manage the approved budget and communicate material cost, schedule, delivery, and residual-risk decisions to executive stakeholders.
- External and Industry Leadership
- Maintain current knowledge of AI security threats, regulatory expectations, industry standards, and relevant technology developments.
- Represent WRBC in selected industry, peer, and strategic-vendor forums and translate external developments into practical internal action.
- Provide clear executive and board-ready communications on AI security posture, priorities, decisions, and progress.