AI Data Security Guides
Guides to the ways enterprise AI systems expose data at the layer beneath the model. Most of that exposure runs through an AI knowledge base, meaning whatever corpus your AI retrieves from at query time, whether that's classic RAG over a vector store, agentic retrieval, GraphRAG or a set of connectors. The main guides define each failure mode in plain language, explain where it happens in a pipeline, and set out the controls and standards that apply, including how each one maps to MITRE ATLAS and what data sovereignty means once a third-party model is in the loop. Below them sit shorter pages answering one narrow question each. Written for security and data engineering teams, and kept current as the regulations move.
AI Knowledge Base Security
Start here for the umbrella view. What counts as an AI knowledge base across classic RAG, agentic retrieval, GraphRAG, connector-based assistants and enterprise AI search, why retrieval is a security boundary rather than a product feature, the four ways a knowledge base leaks, and how to measure your own exposure before choosing controls.
What Is Secure RAG? Securing Your AI Knowledge Base
Secure RAG means the retrieval pipeline enforces the entitlements and content controls of the data it serves. Covers the five pipeline stages, the four routes sensitive data leaks through retrieval, entitlement inheritance failures, indirect prompt injection, and where each control belongs. Applies to any AI knowledge base, not just classic RAG.
Data Poisoning: How It Works and How to Prevent It
How poisoning differs from prompt injection, the four attack classes from NIST's adversarial ML taxonomy, the five lifecycle points where poisoned data enters, and how little an attacker actually needs. Includes the October 2025 finding that roughly 250 documents can backdoor a model regardless of size.
Training Data Leakage, Explained
Why models memorize, the five paths data takes on its way back out, and what extraction attacks actually recover in practice. Explains why holding sensitive fields back destroys the signal teams are training for, and what transformation does instead.
AI Data Pipeline Security
The six stages of an AI data pipeline with the characteristic threat and control at each, why network allowlists and IAM are not positioned to stop data-layer attacks, and how the ML supply chain fits in. Uses the July 2026 Hugging Face intrusion as the worked example.
MITRE ATLAS for AI Data Security
The MITRE ATLAS techniques that sit at the data layer, grouped by failure mode: retrieval leakage, indirect prompt injection, knowledge base and training data poisoning, extraction, and the data supply chain. What each one looks like in an enterprise deployment, the ATLAS mitigations that answer it, and why mapping findings to technique IDs is what makes an AI security posture assessment comparable over time.
Data Sovereignty for Enterprise AI
What data sovereignty means once the model doing the work belongs to someone else. The four routes sensitive data takes out of your boundary (prompts, retrieval context, fine-tuning uploads and provider retention), why enterprise terms and zero-data-retention settings govern what happens after data arrives rather than what arrives, and the control model that keeps a frontier model in the loop while real identifiers stay inside.
Data Silos and Enterprise AI
Silos kept data apart by friction, and that friction was doing access control nobody wrote down. AI assistants and connectors remove it in one query: every over-permissioned site, every cross-silo join, and the collapsed result sent to a third-party model. Why the migrate-first, block-everything and allow-list answers fail, and the five-property design that unlocks data where it lies.
How to Build a Secure AI Knowledge Base
The build procedure. Nine steps from choosing sources by obligation and fixing their permissions, through permission metadata on every chunk and an index kept inside your boundary, to query-time entitlements, identifier substitution, retrieval telemetry and the leakage test that gates rollout. Applies whether the retriever is an index you run, a vendor connector, or an enterprise search product.
For a worked case study of a data-layer failure in production, see our analysis of the July 2026 Hugging Face breach, which entered through a dataset processing pipeline rather than through a model.
AI knowledge base security deep dives
Shorter pages under the Secure RAG guide, each answering one question about retrieval over sensitive data. Classic RAG, agentic retrieval, GraphRAG and connector-based assistants all inherit the same questions.
Building a secure knowledge base
Four pages for the team that is building, buying or evaluating the retrieval system itself. The build procedure is the order of operations, the reference architecture is the design with its trust boundaries and control points, the platform page covers the knowledge features inside ChatGPT, Claude, Copilot and Gemini for teams that upload rather than build, and the GraphRAG and agentic retrieval page covers what changes when the index is a graph or the model plans its own queries.
Framework mapping and posture assessment
Four pages for teams that need to report data-layer findings in the vocabulary of MITRE ATLAS, the OWASP Top 10 for LLM Applications, NIST AI RMF, ISO/IEC 42001 or the EU AI Act. Start with the ATLAS guide if you need to know what to test, the crosswalk if you need to translate a finding between frameworks, the assessment procedure if you need a repeatable coverage record, and the governance page if the question is who owns classification, entitlement and audit for the data assistants retrieve.
Data sovereignty
Five pages for teams that need to answer where sensitive data goes once a frontier model is in the workflow. Start with the definition page if the term itself is in dispute, the third-party platforms page if the question is what ChatGPT, Claude, Copilot or Gemini actually receive, the zero data retention page if a vendor has offered ZDR as the answer, the comparison page if residency, sovereignty and localization are being used interchangeably in a vendor review, and the deployment page if a regulator, a contract or a data classification is deciding where inference may run.
Using enterprise data safely in AI platforms
Twelve pages for teams putting company data into ChatGPT, Claude, Copilot or Gemini. Start with the question page if someone has asked whether company data is safe in a frontier assistant, the provider comparison for what each vendor actually does with it, the three review checklists if a platform is up for approval, the Copilot oversharing guide if SharePoint permissions are the worry, the procedure if a connector is about to go live, the connector page if MCP servers or agent tools are in the picture, the technique comparison for how identifiers are kept out of a model, the DLP page if the plan is to block, the policy template when it is time to write it down, and the data silos guide for why the whole thing leaks in the first place.
Run the test yourself
Secure RAG telemetry is the short, non-technical starting point. It covers what retrieval leakage is, what the open-source client measures, and how to get it running by pointing your own AI assistant at the repository.
The RAG leakage self-test procedure is a self-contained method for measuring whether your own retrieval path returns content a user should not see, using the open-source Hardshell telemetry client.