Secure Every AI interaction

AI Security for Enterprise

Protect employees, AI agents, file uploads, MCP communications, APIs, and every AI interaction with real-time inspection, contextual redaction, and policy enforcement before sensitive data leaves your organization.

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Trusted by

55+

Regulated
Organizations

Illustration of Wald AI DLP inspecting prompts, file uploads, MCP gateway traffic, agent-to-agent communication, and AI tool interactions before enforcing security policies.

Maintains compliance with:

HIPPA

GDPR

CCPA

GLBA

Trusted by Security Teams That Can’t Afford to Get AI Wrong

See how enterprise security leaders are enabling AI adoption without compromising security, compliance, or productivity.

Talk to Founder

Our goal was to enable broad access to top AI tools and LLMs without the friction of competing platforms, bans, or invasive oversight. Wald delivered exactly that. By securely detecting and sanitizing sensitive data in real time, Wald allows our employees to use leading LLMs safely and efficiently. It has streamlined workflows, enhanced visibility into AI usage across the credit union, and provided deep insights through its data classification capabilities.

Chris Chatelain

Vice President of Information Security at EFCU Financial

As AI usage grew across our credit union, we needed to maintain visibility, protect member data, and ensure compliance without slowing employees down. Wald.ai helped us do exactly that. The platform gives us clear insight into AI usage, flags sensitive information, and uses data masking to protect workflows—making oversight significantly easier. This allows our team to securely use different LLMs for their daily needs, boosting productivity while maintaining strict security and governance controls.

Ryan Caruthers

Director of Information Security & ISO at OUCU Financial

Wald has made using AI much easier for our teams. Having access to multiple leading LLMs through a single secure platform means employees can use the right model for the job without juggling multiple subscriptions. The built-in data sanitization and centralized audit logs give us the confidence to adopt AI while supporting our security, governance, and compliance requirements

Jon Scottorn

AVP of Technology, Partner
Colorado Credit Union

Wald.ai with its contextual intelligence addresses a significant need in sanctioned company wide use of generative AI.It offers a thoughtful approach to securing employee AI conversations and safeguarding sensitive private data and our intellectual property

Fatima Afzal

Senior Director Marketing & Comms @PayActiv

It offers a thoughtful approach to securing employee AI conversations and safeguarding sensitive private data and our intellectual property

Donovan Bray

Director of DevOps @Kiavi

Wald enables our employees to safely leverage leading AI models so they can reduce the time they spend on manual tasks. Our traditional DLP was built for email and file transfers and not AI prompts. Wald gave us real visibility and control over how our employees use LLMs without slowing productivity.

Jonathan Antonio

Vice President of Infrastructure @Suki

Ensuring that internal sensitive data remains protected while leveraging AI has significantly enhanced the efficiency and accuracy of legal work without compromising confidentiality or privilege.

Rick Borden

Partner, Data Strategy, Privacy and Cybersecurity in New York, and former Assistant General Counsel at a top 5 US bank.

Backed by Leading Investors

Inventus Capital Partners

Entrada Ventures

MFV Partners

Govern Every AI Interaction
With Confidence

Beyond Pattern Matching

Wald understands context in prompts before flagging, catching what regex rules and pattern-based models were never designed to see.

Multiple AI Interactions

Govern every AI surface from a single platform. Inspect prompts, chats, file uploads, MCP communications, agent-to-agent traffic, AI APIs, and custom AI applications in real time.

Flexible Policy Enforcement

Four actions, one policy engine. Allow, monitor, warn, and block across devices, browsers, data types, teams, and AI apps. Set it once, enforce everywhere.

Build for Enterprise AI Usage

Eliminate Shadow AI Risks in Three Steps

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What is On-Device ‍DLP for AI?

On-device DLP (Data Loss Prevention) is a security approach that detects and classifies sensitive data leakage directly at the endpoint without sending information to external servers.

Wald's on-device DLP provides:

For complete data control, our privately hosted LLM ensures full ownership and security.

Local DLP model with no user visible latency
Complete visibility into all AI interactions
Policy enforcement at the endpoint
No network exposure of sensitive data

What This Means for Your Organization

Regulatory Compliance

Meet data residency requirements. Ensure GDPR/CCPA compliance. Pass your next audit with flying colors.

Complete Visibility

Mitigate shadow AI risks by proactively observing and mapping how employees interact with AI, in real time.

Productivity Unleashed

Let teams leverage AI at full speed. No more choosing between innovation and security.

Risk Mitigation

Prevent data breaches before they happen. Protect your reputation and bottom line.

Board of Directors

Manu Rekhi

Inventus

Karen Roter Davis

Entrada

Aanchal Gupta

Board Advisor. CISO Adobe

Maintains compliance with:

HIPPA

GDPR

CCPA

GLBA

Frequently Asked Questions

How does on-device DLP differ from traditional DLP solutions?

Traditional DLP solutions typically operate at the network level, inspecting data as it passes through gateways. This approach means data is sent to external servers. Wald's on-device DLP operates directly on the endpoint, inspecting all AI interactions locally while they're sent to AI platforms.

How do you set up context-aware DLP with AI?

Context-aware AI DLP is typically deployed as a policy enforcement layer between employees and AI tools using your existing MDM or endpoint management solution. Unlike traditional DLP that relies on static rules, it understands the context of AI prompts and enforces security policies based on the sensitivity of the information being shared. Solutions like Wald AI DLP make it easier for organizations to secure AI adoption without disrupting employee productivity.

How can AI reduce DLP false positives?

AI reduces DLP false positives by understanding the context of the data being shared instead of relying only on keywords or predefined rules. This helps distinguish legitimate business use from actual data exposure, reducing unnecessary alerts and improving employee productivity. Wald AI DLP uses AI-driven policy enforcement to improve detection accuracy while minimizing disruptions to AI workflows.

Which AI DLP tool has the fastest deployment?

The fastest AI DLP solutions are those that work with your existing endpoint management or MDM platform, eliminating the need for complex infrastructure changes. Wald AI DLP can be deployed as a lightweight policy enforcement layer, helping organizations secure AI tools quickly while minimizing IT overhead and user disruption.

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How do AI agents bypass IAM and DLP controls?

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.

What DLP controls are effective for 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.

How does DLP help secure AI tools like Microsoft Copilot?

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.

What is DLP in AI?

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.

How can AI help with DLP investigations?

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.

Can Wald’s AI DLP detect all types of sensitive data?

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.

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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.

How does AI data observability benefit my organization?

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.