Comprehensive
AI Governance

Unified data security governance for generative AI models and applications

Privacera AI Governance (PAIG) product solution

Optimize and modernize with Unified AI Governance

Generative AI and large language models (LLMs) revolutionize enterprise operations and customer interactions. For true success, you must do it securely and responsibly with data privacy, security, and compliance. Revolutionize across your modern data, analytics, and AI enterprise estates with Privacera AI Governance (PAIG).

Protect training data

Training models require vast amounts of data, which must be safeguarded. While internet data poses privacy challenges, fine-tuning AI models using your own company’s data provides tailored value. If this data contains sensitive, classified, or private information, precautions must be taken to prevent breaches and non-compliant use.

Model-response filtering

Applications accessing models for response generation must have appropriate identity authentication and data access permissions. Fine-grained data access controls, such as masking and encryption, must be enforced to prevent the disclosure of sensitive and private information, even if that data exists in the model.

User-prompts filtering

User prompts containing requests for sensitive or private data add another layer of risk. Pre-filtering questions based on users’ security and privacy settings further safeguard data privacy and information confidentiality. This prevents the model from inadvertently disclosing private information.

LLM-powered AI Governance and Security

Built on the existing strengths of Privacera, PAIG combines purpose-built AI and LLMs to drive your dynamic, consistent, enterprise-wide security, privacy, and access governance.

Real-time discovery
and tagging

Training data for generative AI models and embeddings are continuously scanned for sensitive data attributes, then easily tagged. You can leverage more than 160 prebuilt classifications and rules. Your organization can expand on these based on your unique requirements.

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Data access controls, masking, and encryption

Fine-grained data-level controls are established based on your sensitive data discovery and tagging. Efficiently mask, encrypt, and remove sensitive data in your pre-training data pipeline.

Policy-based allow/deny prompts and responses

Real-time scanning of user inputs and queries for sensitive data elements. Apply appropriate privacy controls based on user identity and data access permissions and governance policies.

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Redact/de-identify sensitive data

Real-time scanning for sensitive data elements in model responses. Apply appropriate controls based on user identity and data access permissions.

AI-powered auditing and monitoring

Continuous monitoring and collection of all model usage, user behaviors, responses, and access events into LLMs to drive analytics into usage, security, and risk patterns.