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. 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.
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.
Framework mapping and posture assessment
Three 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, and the assessment procedure if you need a repeatable coverage record.
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.