Today, I'm both honored and excited to share that I've stepped into the role of interim CEO at Cyberhaven, as Howard Ting transitions to our Board of Directors.
AI is transforming productivity across every industry—from marketing and design to legal and engineering. But while employees rush to embrace tools like ChatGPT, Gemini, and Microsoft Copilot, many are using other tools without oversight from IT or security. As this grassroots usage grows, so does the volume—and sensitivity—of data flowing into AI tools. Companies now face a critical challenge: how to capture the enormous potential of AI while managing the risks it can introduce to their data.
As AI becomes deeply integrated into critical business operations and adopted by increasing numbers of departments and employees, the volume and sensitivity of data flowing into these systems has grown exponentially. Companies now face a dual challenge: harnessing AI's potential while managing the substantial data risks it introduces.
Cloud adoption among enterprises accelerated around 10 years ago. During this time, network-based tools emerged as solutions that could protect data as it traveled to the cloud. These solutions, including Security Service Edge (SSE) and Cloud Access Security Brokers (CASB), utilized network-based proxy architectures that could intercept and control traffic. By inspecting data in transit, it ensured that data was controlled according to security policies, effectively preventing exfiltration of confidential data.
We are in the midst of a major technological shift. And when shifts happen, new industry-defining companies emerge. The winners aren’t just those with great ideas—they are the ones who adapt and respond to change the fastest.
Over the years, I’ve learned a few lessons (some the hard way) about how to bring security, GRC, and privacy teams together. At the core of these efforts is a simple truth—data is an organization’s most important asset. Securing and governing it effectively requires a cross-functional approach. Spoiler alert: it’s not just about tools and frameworks; it’s also about people and how we work together.
Every year, companies reevaluate their budgets, making tough calls on where to invest for the most impact. In many organizations, cybersecurity spending is often seen as a cost center. However, without adequate security investments, companies put themselves at greater risk for data breaches that could disrupt business operations and damage customer trust, ultimately costing the company a lot more in the end. To minimize these risks, it’s important to quantify them through risk assessments and metrics. When security investments are tied to business priorities and backed by data, they become easier to justify and far more effective in preventing costly incidents down the road.
Departing workers can pose significant risks to data. Let me share a story about an individual who stole and deleted valuable research data right before submitting his resignation: six weeks after a contingent worker left the company, the FBI contacted us. It turned out that the individual had tried to sell the company’s confidential data to a third party. When he left, everything seemed normal. However, he had transferred some of his work to a personal account before leaving—an activity most companies struggle to detect. Just 24 hours before an employee resigns—or when a mass layoff is looming—data theft spikes dramatically. To reinforce this point, in the 24 hours before a layoff organizations see a jaw-dropping 720% surge in data exfiltration activity compared to the norm. Employees may download sensitive files, forward emails, or copy customer lists—actions that can have lasting consequences, especially if that data ends up with competitors or malicious actors.
The rush to work faster with artificial intelligence (AI) risks encouraging employees to accidentally put sensitive data at risk. Take this scenario: someone in the procurement team has a tight deadline, so they upload a confidential contract into an AI tool to review a few redlines. It’s unclear if the AI system is storing the data from the contract, how long it’ll be retained, and if the data will resurface in a future prompt to someone else. There was no malicious intent here, but there’s no visibility into what happened or will happen to the data and a lack of controls on compliant usage of AI tools. This isn’t just an issue with one department—it’s happening throughout organizations. Employees are using AI tools in the shadows, leaving companies with little control over their data. In this blog, we’ll explore how to manage data exfiltration risks when dealing with unsanctioned AI tools.
Last year, Cyberhaven introduced a revolutionary solution that changed how companies protect their most valuable data: Linea AI. Today, we’re excited to share its progress, success stories, and the advancements shaping its future.
In recent years, we've witnessed an acceleration of consolidation across the cybersecurity industry, with data security at the heart of this evolution. The rise of AI use cases has accelerated the need for protecting sensitive data for most enterprises. Major acquisitions like Dig Security and Laminar in the Data Security Posture Management (DSPM) space, alongside acquisitions of DLP (Data Loss Prevention) companies like Next DLP, Trail, Mimecast, and Code42, signal that we are entering a new phase in how enterprises secure their most sensitive asset—their data.
Securing data today requires the context provided by data lineage: where data came from, who interacted with it over time, which systems have used it, and more. But buyer beware: many vendors now claim to offer “data lineage” that only provides a tiny fraction of the context of true, global data lineage.
In today’s rapidly evolving digital landscape, the ability to see, understand, and control data movement within an organization is more critical than ever. Cyberhaven’s customers are turning to our Data Detection and Response (DDR) platform to power their data security programs, moving away from legacy solutions that fail to offer comprehensive visibility. Cyberhaven stands out by providing unmatched insights into data usage and movement across every part of an organization. From endpoints to browsers and cloud applications, Cyberhaven captures every interaction—every download, modification, upload, and share—and correlates these events in real time to build a complete data lineage. This visibility transforms how organizations manage and secure their data, setting a new standard in data security. Read on to learn how organizations are taking advantage of this visibility to build better data security programs!
Agents can be a pain, we know! From deployment, to managing upgrades, dealing with agent conflicts, and responding to user complaints, we know security teams would rather achieve their objectives without an endpoint agent. But, when it comes to securing your company’s data, there are certain use cases that can only be achieved with an endpoint agent. If you’re not sure if an agent is right for your security program, read on for the top 5 reasons your enterprise needs an endpoint agent for data security.
After economic headwinds caused a downswing in corporate mergers and acquisitions, analysts are projecting an increase in activity in the second half of 2024. This uptick in activity, however, will feature different trends due to the current economic and regulatory climate, with big implications for information security. Read on to learn more about projected shifts in acquisition strategy and the implications for information security!
Real-time feedback on risky behavior stops sensitive data exfiltration and educates employees on security best practices, based on research from Cyberhaven Labs analyzing data on warning and blocking policy implementations.
As the world enters the AI Era, CISOs and CIOs are looking at data security with renewed interest and urgency. Instead of multiple overlapping yet disconnected tools, it’s time for one unified platform to trace and secure data wherever it goes.
In the past decade, organizations seeking to protect sensitive data from negligent or malicious insiders faced two choices: invest in a Data Loss Prevention (DLP) product or an Insider Risk Management (IRM) product. These solutions addressed the same problem from different angles. DLP products focused on analyzing data content to control its movement, while IRM products monitored user behavior for risky actions.
On April 23, 2024, the Federal Trade Commission (FTC) issued a ruling that banned the use and enforcement of non-compete agreements across the United States. With this ruling, enterprises that relied on these agreements to help preserve their competitive advantage must adapt their strategy for protecting proprietary information when an employee departs. Read on for a breakdown of the ruling, what strategies remain open for dealing with this risk, and how security teams can help their organization adapt.
We're excited to announce the introduction of Exact Data Match (EDM) to Cyberhaven’s suite of Data Loss Prevention (DLP) capabilities. Although EDM technology has been in use since the 2000s, Cyberhaven is adapting and advancing this technology to meet the demands of today’s data security challenges. Read on to learn more!
Since ChatGPT launched in November 2022, generative AI has emerged as one of the fastest-adopted technologies in the workplace ever. But, as seen in past paradigm shifts like cloud computing, the productivity benefits of this new technology are balanced with new risks.
Welcome to our Data Security Innovators series, where we talk to security leaders navigating the frontiers of data security with novel processes and technologies.
In the wake of evolving work arrangements, the spotlight often falls on remote and hybrid employees as potential threats to data security. Yet, our latest research uncovers a surprising twist in the narrative. It’s the in-office employees, traditionally considered the safest bet, who are now leading the charge in corporate data exfiltration.
On Wednesday, March 6th, 2024 the US Attorney’s Office of Northern California announced that a federal grand jury had indicted Linwei Ding on four counts of trade secrets theft. Ding was arrested in Newark, California and now faces up to 10 years in prison and a fine of $250,000. We did a deep dive into the indictment to understand what happened and help security leaders and practitioners apply lessons to their own information security practice. Read on to learn more!
Welcome to our Data Security Innovators series, where we talk to security practitioners who are navigating the frontiers of security with novel processes and technologies. In this episode, we speak to Kheun Chan, Principal Security Architect at Iron Mountain. Iron Mountain is renowned as one of the world’s best secure information storage and management services, with 95% of the Fortune 1000 as customers of the company.