Semantic DLP for Enterprise AI

Semantic DLP uses AI to understand the meaning, context, and intent of data when identifying potential sensitive information. Unlike traditional DLP approaches that primarily rely on patterns and predefined rules, Semantic DLP can evaluate the broader context of an interaction.

Traditional DLP commonly uses patterns, keywords, regular expressions, and predefined policies to identify sensitive data. Semantic DLP adds contextual intelligence to understand what the data means and how it is being used.

Context-aware DLP uses AI models to analyze the meaning and context of an interaction and classify whether the information represents a potential data security risk. Policies can then determine whether to allow, monitor, warn, or block the interaction.

AI can evaluate information in context instead of treating every matching pattern as sensitive. This can help distinguish legitimate activity from genuine data exposure and reduce unnecessary alerts.

‍

AI agents can access multiple applications, retrieve enterprise data, and act across systems, creating new paths for sensitive information to move beyond traditional IAM and DLP controls. Organizations need AI-native security that monitors AI interactions and enforces policies in real time. Wald AI DLP helps secure AI agents by applying policy enforcement across enterprise AI usage.

Effective AI DLP combines context-aware policy enforcement, user and application-based access controls, real-time prompt inspection, audit logs, and AI usage visibility. These controls help organizations protect sensitive data while enabling employees to use AI tools securely. Wald AI DLP brings these capabilities together to help enterprises adopt AI without compromising security.

DLP helps secure AI tools like Microsoft Copilot by enforcing policies on the data employees share with AI. It prevents sensitive information from being exposed, provides visibility into AI usage, and helps organizations meet security and compliance requirements. Wald AI DLP extends these protections across multiple enterprise AI tools from a single policy layer.

AI DLP (Data Loss Prevention) protects sensitive enterprise data when employees use AI tools like ChatGPT, Claude, Gemini, and Microsoft Copilot. Unlike traditional DLP, AI DLP understands the context of AI interactions and applies intelligent policy enforcement to reduce data leakage without slowing down productivity. Wald AI DLP helps organizations adopt AI securely across their enterprise.

Since AI can understand the context and intent behind user activity, it helps security teams investigate DLP incidents more efficiently. AI can identify why a policy was triggered, distinguish genuine risks from benign activity, and prioritize high-risk events, reducing manual investigation time. Wald AI DLP provides visibility into AI usage and policy enforcement, helping organizations investigate potential data exposure faster.

Yes. Wald’s AI security agent is designed to detect and classify sensitive information with high accuracy. Unlike tools that rely only on regular expression matching, it understands context and intent, which means it can identify sensitive content even if no explicit marker like a name, email address, or ID number is present.

‍

Out-of-the-box, it recognizes PII (Personally Identifiable Information), PHI (Protected Health Information), intellectual property, source code, and can also be adapted for custom data types unique to your organization. This ensures protection not just against obvious risks, but also against subtle data exposures hidden in conversations or documents.

AI data observability provides complete visibility into how AI tools are being used across your organization. This allows you to identify patterns of sensitive data sharing, understand AI usage trends, and make informed decisions about AI governance policies, all while maintaining compliance with regulations like GDPR and CCPA.