Cyber Manager: AI Data Security

As a Manager: AI Data Security in Deloitte's Cyber for AI team, you will lead client engagements that secure how enterprise data is ingested, transformed, retrieved, and used by RAG and agentic systems. You will translate business and risk objectives into target architecture, remain directly involved in critical design and engineering decisions, and guide teams from assessment through implementation. You will also shape solutions during pursuits and help build repeatable delivery assets.

Recruiting for this role ends on 05/31/2027.

Work you'll do

As a Manager: AI Data Security, you will be responsible for:

• Leading AI data security assessments, architecture workshops, and implementation programs for RAG and agentic systems.
• Translating data use cases and risk requirements into target architectures spanning sources, pipelines, vector stores, retrieval, context, outputs, and memory.
• Remaining directly involved in design reviews, entitlement and metadata decisions, critical pipeline and policy reviews, and resolution of high-risk technical issues.
• Managing engagement scope, teams, milestones, quality, and client expectations while maintaining a clear path from design to production adoption.
• Coordinating data security dependencies across identity, cloud, network, AI engineering, application security, privacy, and operations teams.
• Shaping solutions during pursuits through workshops, demonstrations, proofs of concept, estimates, and delivery plans.
• Developing reusable secure RAG patterns, validation tooling, and delivery methods.
• Coaching practitioners on engineering quality.

The Team Deloitte's Cyber for AI team helps organizations secure generative AI and agentic systems from design through production. The team brings together AI engineering and cyber skills across identity, data, infrastructure, application security, and operations. We work with clients to make security controls part of the platform and delivery lifecycle, so teams can move from experiments to production without creating unmanaged access, data, or infrastructure risk.

Within the team, our Data capability focuses on how enterprise data is discovered, classified, retrieved, transformed, and protected across RAG pipelines, vector stores, model context, and agent memory.

Qualifications

6+ years of combined cybersecurity and AI architecture & engineering experience, including leading production generative AI or agentic implementations across models, orchestration, identity, data, and infrastructure.

Required Experience:

• Designing and implementing data security controls in either AWS, Azure, and/or Google Cloud with experience with data security patterns in the other two platforms.
• Architecting and implementing secure RAG and agentic data platforms across structured and unstructured enterprise sources.
• Vector, search, and data platforms including tenant isolation, document- or row-level entitlements, metadata filtering, and identity-aware retrieval.
• Integrating classification, catalog, lineage, fine-grained access, encryption, key management, tokenization, redaction, and data loss prevention (DLP) technologies into production data paths.
• Reviewing or developing Python and SQL pipelines, access-control logic, validation tooling, and CI/CD integrations.
• Leading client-facing technology or cyber engagements with responsibility for scope, teams, timelines, quality, and executive stakeholders.
• Bachelor’s Degree in Computer Science, Engineering, Information Technology, Cybersecurity or a related field; alternatively, equivalent demonstrated experience.
• Ability to travel 20–50%, on average, based on the work you do and the clients and industries/sectors you serve.
• Limited immigration sponsorship may be available.

Preferred Experience:

• Managed AI services and self-hosted or open-weight AI platforms.
• AI threat modeling, red teaming, security evaluations, secure AI development practices, or AI-specific monitoring.
• Building reusable technical assets, delivery methods, or automation for client engagements.
• Machine learning tools and frameworks such as PyTorch, TensorFlow, or MLflow.
• AI data services such as Amazon Bedrock Knowledge Bases, Microsoft Foundry with Azure AI Search, Google Vertex AI RAG Engine, or Databricks AI capabilities.
• Enterprise data security and governance platforms such as Microsoft Purview, Amazon Macie and Lake Formation, Google Sensitive Data Protection, BigID, Collibra, Immuta, or Privacera.

Certifications (Preferred)

• Certified Information Systems Security Professional (CISSP)
• Certified Cloud Security Professional (CCSP)
• Certified Data Privacy Solutions Engineer (CDPSE)
• AWS / Azure / GCP Cloud certifications
• Databricks and/or Snowflake certification(s)
• Data Engineering certifications

A successful candidate would possess these skills:

• Ability to work independently and collaborate as part of a team
• Effective written and verbal communication skills
• Meticulous attention to detail and quality of work product
• Ability to build and sustain professional relationships
• Ability to lead projects or workstreams
• Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
• Strong interpersonal skills and professional demeanor
• Ability to meet deadlines
• Ability to mentor and provide clear guidance to others

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled.

At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $134,500 to $265,100.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.