Wald.ai

In football ⚽ , you can always tell which team is about to lose. They stop pressing. They sit deep. They protect the scoreline instead of playing their game. And then, almost every time, they concede. The team that shifts from "play to win" to "don't lose" almost always ends up doing the latter. The same thing is happening in enterprise AI security right now. 70% of employees are already using ChatGPT, Claude, and Gemini at work. Source code. Financial forecasts. Customer data. Going straight into public AI tools, with no controls, no visibility, no governance. Most security teams know this. But they're sitting deep. "We'll deal with it when something happens." "We have a policy." "We're monitoring." That's not a defense. That's waiting to concede. The problem with reactive security in the age of AI is that by the time you know data has leaked, it's already in an LLM training set, already in a breach report, already in front of a regulator. You can't un-share a prompt. The teams that win in football and in security are the ones who press from the front. Who create pressure before the threat materializes. Who play offense. That's what Wald is built for. AI-native data loss prevention that stops sensitive data from leaving the endpoint before it ever reaches a public AI tool. Not a policy. Not a review after the fact. Real-time, inline protection so your team can use AI freely while your data stays where it belongs. Don't wait for the incident. Press high. #DLP #AIGovernance #CyberSecurity #DataProtection #CISO #AISecurity #Wald

Traditional DLP wasn’t built for AI. As enterprises adopt ChatGPT, Claude, Gemini, and other LLMs, protecting sensitive data requires more than keyword matching and regex. It requires understanding context. Our latest whitepaper explores what AI-native Data Loss Prevention looks like, including: • Why traditional DLP falls short • The new risks introduced by Generative AI • How contextual intelligence transforms data protection • Key considerations for securing enterprise AI If you’re evaluating AI security or building an AI governance strategy, this is a practical resource for security and IT leaders. 📄 Download the free whitepaper. Link in the comments. Vinay Goel Ritesh Ahuja #CyberSecurity #DataSecurity #DLP #GenerativeAI #EnterpriseAI #AIGovernance

Last week the Wald.ai team got together in Goa for our offsite. Three days of deep discussions, big decisions, and aggressive planning for what’s ahead. The outcome: sharper alignment and a clear focus on building the future of AI Data Loss Prevention. Exciting announcements coming up! #offsite2026

Honest take for CISOs: you can’t govern what you can’t see. Rigid rules and regex will not curb Shadow AI. Even the most sophisticated users can inadvertendly leak data. True guardrails start with visibility. Without it, enforcement is guesswork — and risk accumulates quietly. Would you rather gain transparency now, or during a post-incident review? #endpointdlp

If you blocked ChatGPT tomorrow, half your company would still be using it by Friday. I’ve seen this play out over and over again. A company rolls out a new AI policy. Security locks things down. And within days, people find a way around it. Not because they’re reckless. Because the work didn’t slow down. Someone still has to summarize a contract, respond to a customer. or get a deck out before the meeting. So the AI tools come back in. Quietly. Personal accounts. Browser tabs. Copy and paste. That’s what Shadow AI really looks like. Not rebellion. Just people trying to keep up. From a leadership seat, that’s the part that’s easy to miss. It feels like a policy problem while all along it's a workflow problem. When the only way to move fast is to break the rules, the rules lose. The companies that get ahead here won’t be the ones that ban AI. They’ll be the ones that make safe AI the easiest option. That’s the real shift happening right now. #genai #secureaitips

When the game changes, the gear matters.

I’ve stopped thinking of GenAI as something you “keep up with.” You accept the pace and focus on what doesn’t change. And what doesn’t change is this: Enterprises need to know what data is being shared, where it goes, and whether it should go there at all. The more chaotic AI feels, the more important it is to keep the fundamentals simple. Know the data. Protect the data. Let people work. Everything else is noise. #aifundamentals #genai

Early Christmas launch at Wald, I’m excited to introduce our Desktop DLP Agent. This is the first on-device DLP built for GenAI, with real time policy enforcement and deep data observability across every AI interaction. Simply put, we built a tool that finally gives enterprises control over what goes into their Gen AI subscriptions. It identifies sensitive data, classifies it with high accuracy, and lets you decide exactly what should happen next. Block. Warn and report. Report only. Your rules. Your guardrails. For me, this is the part that matters most. Security teams can finally control how data travels, and they can guide employees toward the right usage patterns without slowing down the work. What I’m most proud of is our classification engine. It understands context, not just patterns. It cuts down false positives and false negatives dramatically. And it surfaces only the data that is truly sensitive. That single shift reduces the load on security teams in a very real way. The Desktop Agent closes a gap I’ve heard about in every enterprise conversation this year. It lets employees use AI confidently, and it gives organizations the control they’ve been asking for. If you want a quick walkthrough or want to test it inside your environment, I’d be happy to show you what we’ve built.