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By Cyberhaven
Most organizations have an acceptable use policy for AI tools. Very few have controls that actually enforce it. The gap between what the policy says and what security teams can detect is where insider risk lives when it comes to large language model (LLM) usage.
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By Cyberhaven
Security teams that deployed legacy DLP years ago built something real. The rules fire. The alerts go out. Compliance boxes get checked. The problem is not that those programs stopped working. It is that the threat moved, and the architecture did not. Agentic AI has introduced a class of data movement that legacy DLP was never designed to govern: autonomous, continuous, multi-step, and operating at machine speed across systems that static rules cannot enumerate in advance.
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By Cyberhaven
Security leaders don't struggle to justify the need for insider risk management (IRM). They struggle to justify the budget. When the CFO or board asks why you're spending seven figures on a program to monitor your own employees, "because insider threats are real" isn't enough. Cyberhaven data shows office-based employees are 77% more likely to exfiltrate sensitive data than remote workers, and that risk spikes further during offsite logins and workforce transitions.
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By Franklin Nguyen
Most data security posture management (DSPM) evaluations start with a deceptively simple question: where does our sensitive data live? There are many tools that answer that question. However, the number of tools that go further by tracking how data moves, enforcing controls when data leaves controlled environments, and closing the gap between visibility and action are far more limited.
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By Iulia Stefoi-Silver
Sensitive data has become the target, the signal, and the source of risk in nearly every modern security program. Source code, customer records, intellectual property, credentials, and regulated data now move continuously across endpoints, cloud apps, SaaS platforms, browsers, collaboration tools, and GenAI applications. That movement is not inherently bad. It is how modern work gets done.
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By Cyberhaven
Data exfiltration is the unauthorized transfer of sensitive data out of an organization's control. It happens across endpoints, cloud applications, removable storage, email, and, increasingly, AI tools. Understanding the most common forms of data exfiltration is the first step toward stopping it.
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By Cyberhaven
Most security programs have more visibility than ever. Dashboards are full. Alerts are firing. And incidents are still happening. That contradiction is not a coincidence. It reflects something most security vendors have quietly avoided saying out loud: Visibility and control are not the same thing, and for a long time, the industry has been selling one while calling it the other.
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By Bruce Chen
Security teams that have invested in AI governance programs over the past two years face a problem that those programs were not designed to solve. The controls built to manage generative AI, network proxies, browser monitoring, and SSO enforcement work when data moves through defined channels. Endpoint AI agents do not move through those channels. They run locally, operate at the OS level, and access data through pathways that exist entirely outside your current visibility.
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By Cyberhaven
AI agents are already running inside your organization. They are accessing files, calling APIs, and executing multi-step workflows with no human reviewing each action. Most governance programs were not designed for this. They were built around policies for human users, controls for known data channels, and audits that happen after the fact. None of those structures were designed to govern systems that act at machine speed across every environment where data lives.
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By Cole Padula
Security teams have a data problem. Not a shortage of data, but instead there is a growing data surfacing problem. The signals are there, the incidents are logged, and the classifications exist. But, getting from raw data to a prioritized action plan still requires close to an hour of manual querying, tab-switching, and context reconstruction, every single time. The Cyberhaven Analyst Plugin changes that.
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By Cyberhaven
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.
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By Cyberhaven
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.
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By Cyberhaven
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.
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By Cyberhaven
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.
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By Cyberhaven
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.
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By Cyberhaven
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.
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By Cyberhaven
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.
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By Cyberhaven
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.
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By Cyberhaven
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.
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By Cyberhaven
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.
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By Cyberhaven
Dive into our expertly curated DLP program checklist that will align with your organization's ambitious business and catapult them forward.
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By Cyberhaven
In this guide we demystify DLP to distill the basics of DLP program development. Learn the essentials required to create scalable data security and data protection programs.
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By Cyberhaven
Data is leaving your company in ways that didn't exist years ago-AirDrop, generative AI, and more. Legacy DLP hasn't kept up; now it's time to invest in more forward-looking solutions.
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By Cyberhaven
DDR makes it possible to stop data exfiltration across all channels with one product and one set of policies.
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Cyberhaven detects and stops the most critical insider risks to your most important data.
Let’s face it, data security products never lived up to our expectations and now that the way we work is changing they can’t keep up. Cyberhaven solves these challenges so companies can finally protect their data.
Data Detection and Response:
- Understand how data flows: See what systems store different types of data and how data moves within the company to new places and people.
- Stop data exfiltration anywhere: Block important data from leaving your control via cloud, web, email, removable storage, Bluetooth/AirDrop, and more.
- Accelerate internal investigations: Quickly understand an incident to determine user intent with a complete record of events before and during an incident.
- Detect and stop risky behavior: Instantly detect when a user handles important data in a risky way, stop them in real time, and coach them.
Trace your data to protect it like never before.