Securing AI: 5 Critical Strategies for Protecting & Governing Data in LLMs

As enterprises rapidly adopt AI/ML and large language models (LLMs), securing the data that fuels these models has become a top priority. Without strong governance, organizations risk data breaches, compliance violations, and AI bias—potentially undermining the very innovations they seek to drive.

According to Gartner’s AI 2024 Planning Survey, 39% of technology leaders cited data protection and privacy as one of the biggest challenges in deploying generative AI—second only to data quality.

At Bedrock Security, we work with enterprises to secure their AI-driven future by addressing the core challenges of AI data security. Below are five key strategies successful security leaders use to protect AI training data—along with how a metadata lake accelerates and strengthens each approach.  

1. Hide the Data: Anonymization & Differential Privacy

AI models often require massive datasets—many containing sensitive or personally identifiable information (PII). Without proper safeguards, this data can be exposed in model outputs or be used to re-identify individuals.

Best Practices:

• Remove PII before data enters AI training pipelines.

• Implement differential privacy—injecting controlled noise into datasets to prevent re-identification while preserving analytical value (for more information tune into Bedrock engineer Saahil Shenoy's upcoming RSA presentation on this topic).

• Use tokenization and pseudonymization to obscure sensitive attributes without reducing data utility.

How the Metadata Lake Helps:

• Automatically classifies AI training data to detect and flag PII before being ingested by models.

• Tracks anonymization status and lineage to ensure compliance with GDPR, CCPA, and other privacy regulations.

2. Map Out the Blast Radius: Entitlements & Access Control

Security begins with who can access AI training data and how much they can see. Poor access controls can lead to overexposed datasets and unauthorized model training, increasing the risk of data leakage.

Best Practices:

• Enforce least privilege access to minimize AI data exposure.

• Use zero-trust principles to verify every AI data request dynamically.

• Monitor entitlement chains to prevent excessive access.

How the Metadata Lake Helps:

• Maps all AI dataset entitlements in real time, showing who has access and how data is being used (our latest blog provides a deep dive into the role of entitlement chains in data risk assessment).

• Identifies over-privileged access and enforces automatic policy corrections.

• Audits access changes continuously, ensuring AI models train only on authorized data.

3. Track Down the Source: Provenance & Lineage Tracking

AI security doesn’t stop at protecting data—it also requires full visibility into where training data comes from, how it is processed, and how it evolves. Without clear provenance, AI bias, security incidents, or compliance failures can go undetected.

Best Practices:

• Catalog all data sources feeding AI models to ensure trust and reliability.

• Track data lineage to monitor transformations and processing history.

• Ensure explainability by maintaining visibility into training datasets.

How the Metadata Lake Helps:

• Captures full AI data lineage, providing a transparent record of data origins and transformations (check out this demo to see how the Bedrock Metadata Lake powers IP data protection through data lineage).

• Links training datasets to AI models, enabling traceability and bias detection.

• Supports regulatory reporting, making compliance audits faster and more effective.

4. Run Nonstop: Regulatory Compliance & Governance

AI data is subject to growing regulatory scrutiny, with laws like GDPR, CCPA, HIPAA, and the EU AI Act imposing strict controls. Automating compliance workflows is essential to avoid costly penalties and reputational damage.

Best Practices:

• Automate compliance enforcement for AI data retention, deletion, and consent.

• Classify and govern training datasets according to regulatory requirements.

• Conduct continuous compliance audits to ensure adherence.

How the Metadata Lake Helps:

• Dynamically applies AI data classification and compliance tags via APIs.

• Assists in regulatory reporting by linking AI training data to governance policies.

• Enables real-time enforcement, preventing non-compliant data from being used in AI models.

5. Get the Facts: Use an AI Data Bill of Materials (AI DBOM)

With AI governance under increasing scrutiny, security teams need a structured approach to tracking AI training data, security controls, and compliance status—similar to how software bills of materials (SBOMs) provide visibility into software components.

An AI Data Bill of Materials (AI DBOM) acts as a comprehensive security inventory for AI models, detailing:

• The datasets used for AI training.

• The security controls applied to data.

• The compliance status of AI data sources.

How the Metadata Lake Helps:

• Automates AI DBOM creation by continuously tracking dataset origins, access policies, and security controls.

• Provides real-time visibility into AI model dependencies and risks.

• Enhances security oversight by linking AI data usage to governance frameworks.

Learn more about the AI DBOM in our recent blog on the topic.

Future-Proofing AI Security with Bedrock Security

As AI adoption accelerates, security and governance must scale alongside it. At Bedrock Security, we specialize in end-to-end AI data protection, governance, and security solutions—empowering enterprises to harness AI safely and transparently.

How is your organization securing AI training data? Learn how top CISOs are protecting their data across cloud and GenAI environments in our latest webinar.