What Data Guard Protects
Data Guard covers the four primary vectors through which malicious content enters your AI system’s data layer.- RAG Corpus Integrity: Data Guard scans your retrieval databases for poisoned or adversarially crafted documents — content designed to manipulate your model’s responses when retrieved, smuggle instructions into the prompt context, or cause the model to produce specific outputs for targeted queries.
- Uploaded File Scanning: When users upload files that enter your AI pipeline — PDFs, spreadsheets, text files, images — Data Guard inspects them before they are indexed or passed to your model. This catches malicious instructions embedded in document content, as well as files that contain sensitive data that should not enter the corpus.
- Embedding Integrity: Data Guard monitors your embedding spaces for anomalous or manipulated vectors. Embedding attacks can cause retrieval systems to surface irrelevant, adversarial, or attacker-chosen documents in response to legitimate queries, effectively injecting content into the model’s context without modifying any readable document.
- Training Data Oversight: Where your pipeline includes training or fine-tuning workflows, Data Guard flags contaminated training samples — examples crafted to introduce backdoors, bias, or targeted behaviors into a fine-tuned model.
How Data Guard Works
Data Guard integrates with your data ingestion pipeline so that content is scanned as it enters your system, before it can influence any model queries.1
Ingestion Interception
When a document, file, or embedding is submitted for indexing, Data Guard intercepts it at the ingestion point — before it is written to your vector store, document database, or training dataset.
2
Adversarial Pattern Scanning
Data Guard analyzes the content for adversarial patterns: embedded instruction sequences, prompt injection payloads, anomalous semantic structures, and known attack signatures. For embeddings, it evaluates vector statistics and neighborhood relationships against your corpus baseline to detect manipulated vectors.
3
Routing Decision
Clean content is approved and passed through to the retrieval pipeline or vector store as normal. Flagged content is quarantined — held in an isolated store where it can be reviewed by your team without affecting live queries — or blocked outright, depending on your operating mode and severity thresholds.
4
Corpus Auditing
In addition to scanning new content at ingestion, Data Guard can perform scheduled audits of your existing corpus to detect poisoned content that was indexed before Data Guard was enabled or before new threat signatures were deployed.
5
Alert and Review
Flagged items generate alerts in the BeyondGuard Control Plane. Your team can review quarantined content, approve it for indexing, discard it, or escalate it for deeper investigation. All decisions are recorded in the audit log.
Configuring Data Guard
Enable and configure Data Guard from the BeyondGuard Control Plane.1
Open Your Project
Navigate to the Control Plane and select the project that manages your AI data pipeline.
2
Open the Guards Tab
Inside your project, select the Guards tab from the left navigation panel.
3
Enable Data Guard
Locate Data Guard in the guard list and toggle it to Enabled.
4
Connect Your Data Sources
Click Manage Data Sources to register the data stores Data Guard should monitor. You can connect vector stores, document databases, file upload endpoints, and training data pipelines. BeyondGuard provides native connectors for major vector databases; for custom stores, use the Data Guard ingestion API.
5
Configure Scan Sensitivity
Under Scan Settings, set the sensitivity level for each threat category — RAG poisoning, file scanning, and embedding integrity — independently. Higher sensitivity catches more subtle attacks but may increase false positives on unusual-but-legitimate content.
6
Set Quarantine Behavior
Under Quarantine Settings, configure what happens to flagged content: quarantine for review, block immediately, or flag and allow (Observation Mode behavior). You can set different behaviors per threat category and per data source.
7
Schedule Corpus Audits
Under Corpus Audit, configure the frequency of scheduled full-corpus scans. For active corpora, a weekly audit is recommended as a baseline.
8
Save and Deploy
Click Save Configuration. Data Guard begins scanning all new ingestion events on this project immediately.
Data Guard works best when integrated at the ingestion point — scanning data as it enters your corpus rather than at query time. Query-time scanning can catch some threats, but it misses persistent poisoning that has already been indexed, introduces latency on every retrieval, and cannot quarantine malicious content before it is served to other users. Connect Data Guard to your ingestion pipeline first, then use scheduled corpus audits to catch anything that entered before integration.