Cyber - Senior Consultant, AI Data Security
deloitteMinneapolis (MN)
Cyber - Senior Consultant, AI Data Security
Posted today
deloitteMinneapolis (MN)
SENIORITY
Lead
About the role
Overview:
In this role you secure how enterprise data is ingested, transformed, retrieved, and used by GenAI, RAG, and agentic systems. You combine advisory with hands-on engineering to shape architecture, integrations, and production controls, while guiding junior practitioners. You’ll lead data-security workstreams and demonstrations, influencing delivery estimates and client outcomes. This position sits at the intersection of AI engineering and cyber, helping teams move from experiments to secure production. You’ll work with cross-functional teams to reduce data and access risk.
Responsibilities:
Lead discovery and data-flow analysis for RAG applications, agents, vector stores, and enterprise sources Design secure data architectures for ingestion, transformation, embeddings, indexes, retrieval, context assembly, caching, and memory Lead integrations with classification, catalog, lineage, DLP, encryption, secrets management, and fine-grained authorization services; engineer entitlement-aware retrieval and metadata-filtering patterns Plan and execute control testing for tenant isolation, access filtering, data leakage, and pipeline reliability; own a data security workstream; manage dependencies and milestones; provide day-to-day guidance to consultants Lead technical demonstrations and prototypes, and contribute architecture, effort, and estimation inputs to pursuits
Key requirements:
Designing and securing RAG or agentic data paths across ingestion, chunking, embeddings, vector indexing, retrieval, prompt context, outputs, and memory; cloud security experience (AWS, Azure, or GCP) Experience with vector, search, and/or data platforms such as Pinecone, Weaviate, Milvus, pgvector, OpenSearch, Elasticsearch, Azure AI Search, Vertex AI Vector Search, Databricks, or Snowflake Data security controls for classification, tagging, lineage, fine-grained access, metadata filtering, encryption, key management, tokenization, redaction, and DLP Building data pipelines with Python, SQL, Spark, Kafka, Airflow; cloud-native data services; Git and CI/CD practices Bachelor's degree in Computer Science, Engineering, Information Technology, Cybersecurity or related field Ability to travel 20%–50%; immigration sponsorship may be available Independent work style with collaborative mindset Clear written and verbal communication Attention to detail and quality of work AWS, Azure, or Google Cloud security patterns Pinecone, Weaviate, Milvus, pgvector, OpenSearch, Elasticsearch, Azure AI Search, Vertex AI Vector Search, Databricks, Snowflake Data security controls: classification, tagging, lineage, fine-grained access, metadata filtering, encryption, key management, tokenization, redaction, DLP
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.