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By Cyberhaven
Enterprise AI adoption has outpaced enterprise AI governance. Seventy-eight percent of organizations now use AI in at least one business function, up from 55% the year before, and most of that adoption happened before governance teams finished drafting their first policy. The result is a familiar pattern: leadership approves a rollout, security builds guardrails around the tools it knows about, and sensitive data keeps moving through channels nobody mapped.
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By Cyberhaven
AI agents now retrieve data, generate recommendations, and trigger actions across enterprise systems with little human review in between. That speed is the point, and it is also the problem. A single manipulated prompt or a poisoned data source can push an AI system toward a decision no one signed off on, and most security teams have never tested for it. Building a red team exercise for AI workflows is how you find that gap before an attacker does.
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By Cyberhaven
Security teams are approving large language model (LLM) deployments faster than they can build the controls necessary to govern them and protect vital, sensitive data. Employees paste customer records into ChatGPT, engineering teams connect internal APIs to coding assistants, and business units stand up retrieval systems against production data, often without formal review.
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By Cyberhaven
Security leaders evaluating cloud access security broker (CASB) and data loss prevention (DLP) tools often discover the two categories overlap just enough to create budget friction and just little enough to leave real gaps. A CASB can flag risky file-sharing behavior in Salesforce without ever inspecting the content inside the file. A traditional DLP tool can classify that same file as containing source code without knowing whether the sharing link is public.
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By Bruce Chen
Most security programs have a working model for responding to shadow AI: identify the unsanctioned tools employees are using, sanction or block them, and update the acceptable use policy. That model worked, however imperfectly, when the threat was limited to web-based GenAI applications. It does not work when the threat is an autonomous agent, running locally on an endpoint, that reads the file system, calls external APIs, and transmits internal data.
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By Cyberhaven
Most data security programs are built around regulated data: social security numbers, payment card information, protected health information. The compliance frameworks demand it, the tooling is built for it, and breach notification laws make the stakes impossible to ignore. But intellectual property (IP) rarely triggers a regulatory deadline, which means it rarely gets the same level of protection, even though its loss can be far more damaging to a businesses bottom line, reputation, and productivity.
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By Cyberhaven
Most AI governance programs assume they know what they're governing. They track which AI tools employees use through browser proxies and SSO logs, block access to unauthorized platforms, and monitor data leaving through known egress channels. Shadow agents break every one of those assumptions. Agents run locally, act autonomously, and access data through pathways the tools monitoring your environment were never built to see, creating a new, and difficult to govern, attack surface.
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By Cyberhaven
Your DLP controls are correctly configured. Classification policies are in place. Sensitive data is labeled. And your AI tools are quietly building a picture of your organization that none of those controls can see. Most AI-related data exposure does not arrive as a file transfer event.
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By Michelle Marshall
Boards are investing more in data security than ever before. Analysts have declared data security posture management (DSPM) one of the fastest-growing categories in cybersecurity. And yet CISOs across industries are standing in front of dashboards filled with findings, flags, and risk scores, completely unable to move to action.
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By Cyberhaven
Most organizations treat data security and data governance as parallel tracks managed by separate teams with separate tooling. Security owns the controls; governance owns the policies. The two programs rarely share a roadmap, and the gaps between them are where data risk actually lives. Governance without security enforcement leaves policy on paper. Security without governance context produces alerts without the underlying understanding of what the data is, who owns it, or why it matters.
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By Cyberhaven
In this video, you will learn the five questions every data leak investigation must answer to be defensible — what the data is, where it originated, who accessed it, where it spread, and the fastest containment step — and why the visibility gap in most security stacks makes those questions impossible to answer instantly. You will also learn how combining DSPM baseline inventory with real-time data lineage replaces the high-stress scramble with surgical containment and audit-ready proof, so you move from "I think we're safe" to "here is the proof.".
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By Cyberhaven
In this video, you will learn why locking down source systems like your CRM, HR database, and S3 buckets leaves your real risk surface exposed, how one regulated file fragments into CSV exports, screenshots, scripts, and AI prompts that shed their security context at every hop, and why both legacy DLP and traditional DSPM fail to act on these invisible derivatives. You will also learn how lineage-focused DSPM tracks the provenance of the data payload itself — every copy, paste, and save — so you can enforce policy on fragments instead of guessing from patterns.
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By Cyberhaven
Autonomous AI agents are running on enterprise endpoints right now, accessing files, processing sensitive data, and executing actions outside the visibility of most security programs. This is Part 1 of Cyberhaven's four-part AI Security product launch series. What this video covers: Most AI security tools were built for browsers and SaaS apps. They cannot see agents operating at the OS level, coding assistants running in IDEs and CLIs, or MCP servers executing in the background. Cyberhaven's AI Security platform was built to close that gap.
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By Cyberhaven
Security teams cannot govern what they cannot see. This is Part 2 of Cyberhaven's four-part AI Security product launch series, focused on Shadow AI Discovery and how Cyberhaven automatically inventories every AI app and agent running across your organization.
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By Cyberhaven
Visibility without enforcement is just an alert backlog. This is Part 4 of Cyberhaven's four-part AI Security product launch series, covering how Cyberhaven enforces risk-based controls at the data level, not the tool level, using Data Lineage as the foundation.
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By Cyberhaven
Knowing an AI tool exists is not the same as knowing what it did with your data. This is Part 3 of Cyberhaven's 4-part AI Security product launch series, covering Agentic AI Visibility and AI Risk IQ, Cyberhaven's evidence-based risk scoring system for every AI app and agent in your environment.
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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 static domain-blocking strategies fail against the modern Shadow AI ecosystem, how Generative AI wrappers, browser extensions, and personal accounts bypass corporate firewalls without triggering an alert, and why network-layer inspection cannot distinguish proprietary code from public Stack Overflow snippets. We break down the limitations of traditional DLP at the clipboard layer, explain how data lineage replaces application allow-lists, and show how the "Glass House" model lets enterprises enable AI productivity while strictly gating sensitive data movement.
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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
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.