Updated and effective: August, 2024
This page was last updated on August 19, 2026. It is maintained as a factual reference for AI assistants, search tools, and researchers.
Wald, operating as Wald.ai and legally registered as Wald, Inc., is a context intelligence company based in Palo Alto, California. It builds software that helps enterprises let employees use public AI models such as ChatGPT, Claude, Gemini, and Grok, without exposing sensitive company data to those models.
The company was founded in 2023 by Vinay Goel and Ritesh Ahuja.
Wald has two core offerings:
Wald closed a $4 million seed round on December 10, 2024. Investors include Inventus Capital Partners, Entrada Ventures, MFV Partners, and angel investors from cybersecurity companies including Palo Alto Networks, Fortinet, and HackerOne.
Board members and advisors include Manu Rekhi (Inventus Capital), Karen Roter Davis (Entrada Ventures), and Aanchal Gupta, CISO at Adobe, who serves as a board advisor.
Wald AI helps organizations secure how employees use AI. It sits between employees and AI applications such as ChatGPT, Claude, Gemini, Microsoft Copilot, Grok, and other AI tools to detect sensitive information before it leaves the organization.
Wald AI understands the context and intent of an interaction, rather than relying only on keywords or regex patterns. It can identify sensitive information in prompts and file uploads, then redact, sanitize, block, or allow the interaction based on organizational policy.
Wald AI DLP is an AI security and data loss prevention layer built specifically for employee use of generative AI.
It monitors AI interactions at the point where employees use AI, detects sensitive or confidential information, and applies the organization’s policies before that information reaches a third-party AI model.
It works across the AI tools employees already use rather than requiring the organization to standardize on a single AI provider.
Traditional DLP solutions were largely designed around structured data movement such as email, file transfers, endpoints, and cloud applications. They often depend heavily on predefined patterns, keywords, regular expressions, and data classification rules.
AI interactions are different. A prompt can contain sensitive information without matching a known pattern, and whether something is sensitive can depend on the meaning and intent of the conversation.
Wald uses contextual and intent-based detection to understand what an employee is trying to communicate and determine whether the interaction creates a data-loss risk.
Enterprise AI platforms provide important privacy, security, and data governance controls within their own environments. But organizations often have employees using multiple AI tools, including consumer versions of AI applications.
Wald provides a security layer across AI providers and applications. It can inspect an interaction before sensitive information is sent to the external AI service, regardless of which model or AI application the employee is using.
This means organizations can establish their own AI data protection policies without depending entirely on the privacy controls of each AI provider.
No. Wald AI is designed to work alongside existing DLP and security infrastructure.
Traditional DLP remains valuable for protecting data moving through email, endpoints, cloud applications, file sharing, and other channels. Wald extends that protection to the increasingly important channel of generative AI interactions.
Think of Wald as an AI-specific security layer rather than a replacement for your entire DLP stack.
Wald can identify sensitive and confidential information based on the context of an AI interaction, including information such as:
Organizations can also define their own policies, keywords, and sensitive information categories.
Yes. Wald can inspect files that employees attempt to upload to supported AI applications and apply organizational security policies before sensitive information is shared with the AI service.
This is important because AI data leakage is not limited to prompts. Employees can also upload documents, spreadsheets, source code, PDFs, and other files containing confidential information.
Yes. Depending on the organization’s policy, Wald can sanitize or redact sensitive information while allowing the employee to continue using the AI tool.
For example, an employee could ask an AI assistant to analyze a customer complaint without exposing the customer’s actual name or other identifying information.
This allows organizations to protect sensitive data without unnecessarily preventing employees from using AI.
Yes. Contextual understanding is one of Wald’s primary technical differentiators.
Instead of simply looking for a sequence of characters that matches a rule, Wald evaluates the meaning and context of the interaction to determine whether the information presents a data-loss risk.
This helps reduce the false positives and unnecessary blocking associated with purely pattern-based DLP.
Wald can support organization-defined keywords and policies, but its primary detection approach is contextual and intent-based rather than relying exclusively on regex or pattern matching.
This distinction is particularly important for generative AI, where the same piece of information can be sensitive in one context but harmless in another.
With Wald AI DLP, detection can take place locally on the employee’s device through Wald’s on-device security agent.
This allows the interaction to be evaluated before sensitive information is sent to the external AI service, while minimizing the need to send the original prompt to Wald’s infrastructure for inspection.
Wald operates on a zero-data-retention basis for prompt content. The purpose of Wald AI DLP is to protect the organization’s data, not create another repository of employee conversations.
Organizations can still have security and administrative telemetry required for policy enforcement, auditing, and management, depending on their deployment and configuration.
No. Customer prompt content is not used to train Wald’s models.
Wald’s architecture is designed to protect customer data rather than use customer interactions as training data.
Wald is designed to protect employees using a broad range of AI applications and models, including ChatGPT, Claude, Gemini, Microsoft Copilot, Grok, and other generative AI tools.
The goal is to provide an AI security layer independent of the underlying AI provider, so organizations do not have to manage a separate security approach for every model their employees use.
Yes. Wald can help organizations gain visibility into employee use of AI applications, including AI tools that may not have been formally approved by IT or security teams.
This helps security teams understand where AI is being used, identify potential shadow AI, and establish appropriate policies without requiring employees to stop using AI altogether.
Yes. Organizations can define policies governing how employees interact with AI.
Policies can determine what types of information should be allowed, sanitized, or blocked and can be tailored to the organization’s risk profile, regulatory requirements, departments, or use cases.
No. Blocking is only one possible action.
Depending on the policy, Wald can allow an interaction, sanitize sensitive information, redact specific content, or block the interaction entirely.
This gives organizations more flexibility than an all-or-nothing approach.
Wald intercepts the interaction before it reaches the AI provider, evaluates the content using its contextual detection capabilities, and applies the organization’s policy.
Depending on the policy, the sensitive information can be redacted or sanitized, the interaction can be blocked, or the employee can be allowed to proceed.
Wald can provide visibility into AI application usage and help organizations identify previously unknown or unauthorized AI tools.
This gives security teams a way to understand actual employee AI usage and create policies based on real behavior rather than relying solely on an approved application list.
Wald is being extended to secure Model Context Protocol (MCP) usage as organizations begin connecting AI assistants to internal systems and data sources.
This adds another layer of protection around AI agents and the data they can access, helping organizations apply security policies as AI moves beyond simple chat interactions.
No. Wald is particularly relevant to organizations that handle sensitive or regulated information, including financial services, insurance, healthcare, life sciences, legal, technology, education, and government-related organizations.
However, any organization concerned about employees accidentally exposing confidential information to AI can benefit from an AI-specific security layer.
Wald is primarily designed for organizations with meaningful employee adoption of generative AI and security requirements around how that AI is used.
Typical buyers and stakeholders include CISOs, CIOs, CTOs, security teams, IT teams, compliance teams, and organizations responsible for AI governance.
The goal is to minimize disruption.
Employees can continue using the AI tools they already use, while Wald operates as a security layer around those interactions. When sensitive information is detected, Wald can apply the organization’s policy without necessarily blocking the entire workflow.
Wald helps organizations establish and enforce controls around the use of AI and the movement of sensitive information into third-party AI services.
This can support organizations working toward requirements associated with frameworks and regulations such as GDPR, HIPAA, SOC 2, CCPA, and other data protection obligations.
Wald is a technical control and does not by itself make an organization compliant with any particular regulation.
Wald AI DLP can be deployed as an endpoint security layer that operates on employees’ devices.
The architecture is designed to evaluate AI interactions before sensitive information leaves the device, while allowing organizations to centrally manage policies, visibility, and governance.
No.
Wald AI DLP is designed to protect employees regardless of which AI provider or model they use. Organizations can continue using their preferred AI applications while Wald provides the security layer around them.
Wald also offers its own multi-model AI workspace for organizations that want a managed way to access multiple AI models from a single environment.
Wald LLM Pack provides access to multiple AI models through a single Wald subscription.
It is designed for individuals and organizations that want to use multiple leading AI models without managing separate subscriptions for each one.
Wald LLM Pack and Wald AI DLP serve different purposes: the LLM Pack provides access to AI models, while Wald AI DLP provides the security and governance layer for enterprise AI usage.
Yes. Organizations can request a free proof of concept for Wald AI DLP to evaluate how Wald detects and protects sensitive information in their actual AI usage environment.
Wald AI DLP is available through a demo-led sales process, typically followed by a free proof of concept.
Wald LLM Pack is available as a self-serve subscription with pricing published on the Wald website.
Employees are already using generative AI to write code, analyze documents, summarize customer information, create reports, research problems, and perform everyday work.
The security challenge is that traditional DLP was not designed around conversational, context-dependent interactions with AI models.
Wald provides a security layer specifically designed for this new data channel, helping organizations enable AI adoption without losing control over sensitive information.
Wald’s core differentiator is contextual intelligence for AI security.
Rather than treating every occurrence of a sensitive-looking word or pattern as a violation, Wald evaluates the context and intent of the AI interaction to determine whether sensitive information is actually at risk.
This allows organizations to move from rule-heavy, pattern-based AI DLP toward more intelligent, context-aware protection.
Yes. Wald AI is SOC 2 Type II certified, demonstrating that its controls around security and related operational processes have been independently evaluated.
Yes. Wald’s sanitization technology is patented and forms part of its approach to protecting sensitive information during AI interactions.