AI security and governance

Secure the complete AI data path—not only the model endpoint

Design identity, retrieval permissions, data handling, model controls, logging, evaluation, and operational ownership as one coherent system.

Review your AI risk

Source-connected

Permission-aware

Measured before launch

RAG changes the security boundary

A RAG application connects users, source systems, indexes, application logic, models, tools, logs, and administrative interfaces. Sensitive information can leak through retrieval, prompts, responses, traces, caches, exports, or overly broad support access.

Security therefore starts with a data-flow and threat model. Controls must follow information from ingestion through deletion and account for malicious content, compromised credentials, authorization mistakes, model behavior, and operational access.

Core controls for enterprise AI

✓

Identity integration and least-privilege access

✓

Repository and document-level authorization at retrieval time

✓

Encryption and approved regional or tenant boundaries

✓

Secrets management and isolated service identities

✓

Prompt-injection and untrusted-content defenses

✓

Input, output, and tool-use constraints

✓

Redaction and handling rules for sensitive data

✓

Audit logging, retention, incident response, and deletion workflows

Governance that supports delivery

Useful governance defines owners, approved use, evaluation thresholds, review requirements, change control, monitoring, and escalation. It should distinguish low-risk productivity assistance from decisions that affect customers, rights, safety, finances, or regulated obligations.

Compliance is use-case specific

HIPAA, privacy laws, contractual duties, professional obligations, and industry controls impose different requirements. We help technical and accountable business teams document the intended use, data categories, vendors, access model, testing, and operational controls. Legal and compliance conclusions remain with your qualified advisors.

Threats specific to grounded and agentic AI

✓

Unauthorized retrieval caused by missing or stale permissions

✓

Prompt injection embedded in retrieved documents

✓

Sensitive data copied into prompts, logs, traces, or caches

✓

Model or tool actions performed with excessive privileges

✓

Cross-tenant or cross-matter information exposure

✓

Poisoned sources and manipulated ranking

✓

Unsupported answers presented with misleading citations

✓

Unreviewed component changes that alter safety behavior

Questions

Frequently asked questions

Neither approach is inherently secure. RAG can keep knowledge in controlled sources and apply retrieval permissions, but it also introduces indexes, connectors, prompts, logs, and authorization paths that must be secured.

The application should enforce identity and access filters during retrieval, not rely on the model to hide information after it has been retrieved.

Prompt injection occurs when untrusted instructions in user input or retrieved content attempt to change system behavior, expose information, or trigger inappropriate actions. Defenses require layered design and testing.

A practical next step

Bring us the workflow that is stuck—not a finished AI specification.

We will help clarify the opportunity, data, risks, and smallest useful way to prove value.

Plan a project