Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In this video, you will learn how lightweight OS-level instrumentation binds lineage metadata to clipboard content the moment data is copied, how that tag survives edits, reformatting, and translation across applications, and how provenance-based policy replaces pattern matching with precision rules tied to the actual source of the data. You will also learn how pairing network tools with a browser extension captures user intent before encryption, eliminating the alert fatigue that buries real risk in noise.

In this video, you will learn why agentic browsers like ChatGPT Atlas, Perplexity Comet, and Arc have turned the browser into a double agent inside your enterprise, how shadow adoption is bypassing MDM and endpoint controls in days, and why indirect prompt injection creates an attack surface your file-based DLP cannot see. You will also learn how data lineage replaces noisy content inspection with origin-and-destination tracking, so you can stop the leak without blocking the tools your business depends on.

In this video, you will learn why legacy DLP tools go blind when sensitive data is copy-pasted into generative AI tools, how Data Lineage fingerprints information at its origin to track it across transformation within an environment, and how operating system-level monitoring eliminates the encryption blindness that limits browsers and firewalls. You will also see how to build context-aware paste policies that allow productive AI use while blocking high-risk data flows from sources like source code repositories, Salesforce, and internal wikis.

Your developers are leaking IP into generative AI— and your DLP can't see it. This is the Shadow AI gap breaking legacy Data Loss Prevention's capabilities.

On this episode of Founder Stories, Nishant Doshi, Cyberhaven CEO, and Dr. Volodymyr Kuznetsov, Co-founder and Chief Technology Officer at Cyberhaven, join the show to discuss their transition from founder-led leadership.

AI is rewriting data risk. On Feb 3, see how to fight back. Every week, AI makes your team faster—and your data more exposed. Files jump between new tools, models train on sensitive inputs, and traditional DLP is blind to the context that matters most. On February 3 at 11:00 AM PST, we’re pulling back the curtain on Cyberhaven’s unified DSPM & DLP platform—and showing how a single, AI‑native platform can finally keep up with how data actually moves.

Cyberhaven is excited to introduce Data Security Posture Management, now in Early Access. Existing DSPMs helped security teams inventory sensitive data across cloud repositories, but they stop short of delivering meaningful protection. They identify what data organizations have and where it resides, but not who owns it, where it came from, or how it’s being used. As data moves through modern organizations, copied between applications, repos, and endpoints, summarized into AI tools, and shared externally, those systems lose visibility and therefore their ability to protect data.

Resolve incidents 5x faster, detect 40% more critical incidents, and reduce future incidents by 90% with Linea AI by Cyberhaven. Linea AI thinks like the smartest security analyst, precisely spotting insider risks across billions of workflows and every piece of data. It understands how people work the way a human would, but it never loses focus and can apply human-like insight at an incredible scale.

In this video, we break down these two important but often-confused terms in cybersecurity. Insider risk refers to the potential for harm that comes from employees, contractors, or partners who have access to sensitive data — whether accidental or intentional. Insider threat is when that risk becomes an actual malicious or negligent action that puts your organization at risk.

In this video, we explain the basics of insider risk management — the practice of identifying, assessing, and reducing the risks that come from employees, contractors, or partners who have access to sensitive data. Insider risk management goes beyond traditional data loss prevention by addressing both malicious and accidental insider threats. From protecting intellectual property to preventing data leaks, insider risk management helps organizations secure their most valuable information.