The IT Nerd

MIND, in partnership with the CISO Executive Network, today announced new research, The Impact of Data Trust on AI Initiative Success, which examines the role of data trust in AI success. The findings point to a widening gap between rapid AI adoption and the ability to secure and govern the data that powers it.

AI is already embedded across the enterprise. According to the report, 90% of organizations are running enterprise GenAI at scale, yet 65% of CISOs lack confidence in their data security controls and only 20% of AI initiatives meet their intended KPIs.

The research introduces a clear insight: data trust is the degree of confidence that systems, including AI, use data safely and appropriately. When that trust is high, organizations move faster. When it is not, AI slows, stalls or introduces risk that outweighs its value.

The study, based on a survey of 124 CISOs and in-depth interviews with senior practitioners, highlights several consistent patterns. Organizations have policies for AI, but struggle to enforce them at machine speed. Data estates remain unclassified and ungoverned. Security frameworks were built for human behavior, not autonomous systems. The result is measurable failure, not theoretical risk.

Nearly two thirds of CISOs report low confidence in their ability to prevent unsafe AI data access. At the same time, business pressure to accelerate AI adoption continues to increase, compounding exposure.

The report frames AI as a stress test of existing security fundamentals. Organizations with strong data foundations are positioned to accelerate. Those without face a growing risk of failure, including stalled initiatives, regulatory exposure and potential business disruption.

At its core, the research reframes data security as a business enabler. As companies embrace AI innovation, high data trust moves beyond protection to become a competitive accelerant.

MIND’s perspective reflects this shift. The company positions data security not as a barrier to AI, but as the condition that makes AI viable at scale. By enabling organizations to understand, control and act on data risk in real time, MIND supports a model of Stress-Free DLP, where security operates with the speed and precision that AI demands.

The full report, “The Impact of Data Trust on AI Initiative Success,” is available now.

Enterprises are moving quickly to adopt agentic AI to drive real business outcomes, including faster decision-making, increased productivity and new operational efficiencies. But as AI systems become more autonomous, those outcomes depend on one critical factor: whether organizations can trust how their data is accessed, used and controlled.

Today, MIND announced DLP for Agentic AI, a data-centric approach to AI security designed to help organizations safely achieve the business value of agentic AI by ensuring sensitive data and AI systems interact safely and responsibly.

Agentic AI can autonomously create, access, transform and share data across SaaS applications, local devices, homegrown systems and third-party tools. While this unlocks meaningful gains in speed and scale, it also introduces new risks. Without clear visibility and controls, data security gaps can undermine AI initiatives, slow adoption and put business outcomes at risk.

Data Security as the Foundation for AI Outcomes

As organizations evaluate how to secure agentic AI, new security categories are appearing. However, most of these emerging approaches fail to secure the critical foundation that Agentic AI relies on: the data itself.

MIND’s DLP for Agentic AI starts with the belief that business outcomes depend on whether AI systems have the right access to the right data at any point in time. Instead of securing models or reacting to outputs, MIND ensures sensitive data is understood, governed and protected before any AI agent can access or act on it.

With this data-centric approach, organizations can:

• Identify which AI agents are active across the enterprise and on endpoints, including embedded SaaS capabilities, homegrown agents and third-party tools
• Detect risky data access by AI agents, monitor behavior in real time and autonomously alert and remediate issues as they emerge
• Apply the right controls so data and agentic AI interact safely, without slowing productivity or innovation

By putting data security and controls at the center of AI adoption, MIND helps organizations turn AI potential into measurable business results with the right guardrails.

Customers are already using MIND to support enterprise AI initiatives and the secure use of GenAI while maintaining strong data security.

Built for an Agentic AI World

Traditional DLP programs were designed for predictable, human-driven workflows. Agentic AI operates differently, moving at AI speed and acting autonomously. MIND’s DLP for Agentic AI brings context-aware automation to data security, helping teams prevent risk before it impacts the business.

As organizations continue to invest in agentic AI, MIND positions data security and controls as the missing piece required to achieve AI-driven outcomes safely and sustainably.

To learn more about DLP at AI speed and how MIND enables secure, outcome-driven AI adoption, visit mind.io.

MIND today announced new innovations to endpoint data protection. These AI-native DLP capabilities redefine how enterprises prevent data loss, on every user device, across every environment, with unmatched content- and context-awareness. As GenAI usage continues to expand, it’s every organization’s imperative to stop data leaks on endpoints without disrupting productivity and innovation.

As the most immediate and active touchpoint for sensitive data, the endpoint plays a pivotal role in the data security lifecycle. Endpoint DLP has historically been one of the most challenging areas of data protection, prone to noise, complexity, blind spots and user friction. But it’s also where some of the most critical risks originate. From accidental file uploads to intentional data exfiltration, the endpoint is where sensitive data is most often handled, manipulated and moved. MIND addresses these risks head-on with a platform that detects risk in real time and responds automatically with dynamic, policy-based remediation and prevention. MIND gives security teams a more accurate, proactive way to stop data leaks before they happen.

MIND’s new endpoint innovations deliver enhanced controls to its award-winning platform, one built for the future of data protection in the AI era, where visibility, context and automation are seamlessly connected. Built on MIND’s unified platform that spans discovery, classification, detection, remediation, policy management and prevention, this expansion brings the same level of simplicity and automation to the protection of endpoints and the data they process. With this announcement, MIND becomes the first in the industry to combine built-in advanced AI data classification, risk remediation and now modern enterprise endpoint protection.

Endpoint data protection and more, upgraded

MIND is transforming endpoint DLP into a less stressful part of the data security lifecycle. Legacy tools have long been complex, noisy and disconnected, yet the endpoint remains one of the most critical control points in modern data security. It’s where AI tools interact with sensitive data and where it is most vulnerable. This expanded approach replaces friction with automation, noise with intelligence and complexity with a simple, stress-free approach. These platform enhancements are designed to simplify and fortify sensitive data protection at the edge.

MIND’s Endpoint DLP elevates the experience with:

• Full Data Lineage: Track every sensitive file’s journey across users, devices, origins and destinations.
• Native App Protection: Protect data used inside locally installed applications without agent sprawl or user disruption, including GenAI apps.
• USB and Peripheral Controls: Automatically govern and stop data leaks to external devices connected to the endpoint with precision.
• Evidence Collection: When triggered by policy violations, capture screenshots, file actions, user behavior and more for investigations and audits.

Today, MIND announced the appointment of Jimmy Tsang as Chief Marketing Officer, whose leadership will be crucial in scaling the company’s global presence, driving revenue growth, and solidifying MIND’s brand positioning as a rising force in DLP. 

Since joining MIND in 2023, Tsang has led the company’s strategic branding efforts, significantly enhancing its market presence. With 2+ decades of experience in cybersecurity and marketing, Tsang previously served as VP of Marketing at Pondurance and led both product and content marketing for IBM Security.

This announcement comes amid a period of accelerated growth for MIND, driven by customer adoption already serving Fortune 1000 companies across diverse industries, strategic partnerships, and industry accolades. 

Recently, MIND announced $30 million in growth funding, bringing total funding to over $40 million. At this year’s Black Hat, MIND launched the first autonomous DLP platform and earned Honorable Mention in its Startup Spotlight Competition.

MIND today announced the general availability of the first autonomous DLP platform. Designed for security teams to allow organizations to safely use GenAI, go beyond compliance and finally make DLP useful, MIND puts data protection on autopilot, covering every IT environment, reducing manual work and stopping sensitive data leaks before they happen.

As the first AI-native DLP platform built from the ground up to automate the entire lifecycle of data protection, MIND delivers:

• Industry-leading data discovery: Automated and continuous inventory of sensitive data at rest and user/agentic AI/non-human activities to remove data security blind spots.
• Autonomous, AI-powered classification: 91% more accurate than legacy DLP tools, eliminating alert fatigue and false positives with MIND AI, a multi-layer classification engine that goes beyond RegEx pattern matching and uniquely categorizes sensitive file types like never before.
• Business-aligned policies: Simple, intuitive policy creation with out-of-the-box templates to achieve faster time-to-value.
• Effortless remediation: Automated responses, guided workflows and integration with current remediation platforms to reduce data security risks and exposure.
• Secure data at rest and protect data in motion: MIND actively prevents leaks wherever data lives or moves across IT environments, including GenAI, SaaS, endpoints, emails and on-premise file shares, eliminating blind spots due to legacy DLP silos.
• User-centric prevention: Real-time, context-aware controls that help users follow security policy, not just block them, dramatically reducing the user friction caused by traditional DLP tools.
• Rapid time-to-value: With a simple deployment, the MIND platform quickly brings real security value to organizations in days, not months.

Data security posture management isn’t enough – just knowing where your sensitive data lives doesn’t keep it safe. Security teams need real prevention, not just visibility and orchestration. As corporate data volumes skyrocket and organizations race to adopt cloud, SaaS and GenAI tools, legacy DLP solutions and modern posture management tools have failed to keep up. Today’s security teams are buried in manual work, false positives and operational complexity, while 73% of sensitive data remains exposed, according to our recent research with TechTarget’s Enterprise Strategy Group (ESG), a leading IT analyst, research and strategy firm.

Security teams today face an overwhelming amount of stress trying to keep pace with DLP alerts. Almost 92% of DLP alerts are false positives, not addressed in 24 hours or never remediated at all. An automated approach to data security, one that brings discovery, classification, remediation, policy management and prevention together, is the key to protecting sensitive data and lowering the stress security teams face. It’s time for stress-free DLP.

Key Industry Insights:

• 78% of organizations find DLP administration challenging
• Only 27% of sensitive data is properly discovered and classified
• On average, enterprises experience 4.2 known sensitive data loss events per year, despite using multiple DLP tools

MIND customers have seen DLP management time drop by 80%, false positives cut by 95% and results delivered within hours of deployment, including uncovering critical data risks and blocking exfiltration of sensitive data.

MIND is the first platform to unite data discovery, AI classification, policy management, automated remediation and intelligent prevention, all in one, easy-to-deploy-and-manage solution. With coverage for GenAI, SaaS, endpoints, emails and on-premise file shares, MIND helps organizations mind what matters and regain confidence in their data security.

MIND is on a roll with industry recognition in 2025, recently being named one of Fortune’s Top 50 Cybersecurity Companies of 2025 in partnership with Evolution Equity Partners, underscoring MIND’s exceptional leadership in the cybersecurity industry and the only DLP solution on this prestigious list. Selected from hundreds of nominees, this inclusion in the Top 50 reflects a rigorous evaluation by a panel of top investors and cybersecurity experts. The judging criteria included technical innovation, operational excellence and a steadfast commitment to strengthening global cyber resilience.

Recently recognized as an Honorable Mention by the expert-led judging panel of the Black Hat Startup Spotlight Competition, MIND stood out among hundreds of submissions to identify the brightest new stars that have demonstrated innovation to address today’s critical cybersecurity challenges. MIND is one of only two startups that have been honored in the RSAC™ 2025 Conference 20th Annual Innovation Sandbox Contest and Black Hat Startup Spotlight Competition. Experience MIND’s award-winning, stress-free DLP platform at Black Hat booth #4833 from August 6 to 7.

The MIND autonomous DLP platform is available now. To learn more or schedule a demo, visit https://mind.io/product.

MIND™ today announced the release of The State of Data Loss Prevention – Current Struggles and Future Expectations. The report examines trends driving the need for data loss prevention (DLP) solutions to secure sensitive information from unauthorized access, leakage and theft, and key challenges as enterprise security teams struggle with outdated or incomplete tools. The report’s findings underscore the importance of modernizing DLP programs so that organizations can efficiently scale sensitive data visibility, classification, detection, remediation and loss prevention.

The report found that enterprise environments are more complex and data stores are exponentially growing, further exacerbating security team difficulties, such as maintaining and evolving DLP policies, dealing with a majority of alerts that are false positives and lack of resources to address and investigate every incident. In fact, 78% of organizations report being challenged in administering and maintaining existing DLP technology solutions and policies, and 94% report using at least two tools and, on average, more than three tools with DLP capabilities, resulting in significant man-hours to administer and maintain multiple solutions. Additionally, nearly all organizations (91%) said it is important to reduce alert noise produced by their current DLP controls due to simple, poor and outdated classification schemes.

These challenges highlight the importance of adopting a future-ready DLP strategy that autonomously discovers and classifies sensitive data that matter, proactively detects issues with a context-aware and risk-based approach and automatically prevents and remediates data leaks. By delivering on these modern capabilities, organizations can expect to experience unprecedented visibility and understanding of their data risks, simplified solution management, dramatic reduction of false positives and efficient data loss prevention and issue remediation.

The report’s key findings include:

• Persistent data leaks: Despite using multiple DLP tools, 53% of respondents reported two or more unstructured data loss events that they know of and, on average, more than four data loss events in the last 12 months. There were likely many more data loss events that are unknown.
• Lack of visibility and understanding of data risks: Organizations report that more than 73% of their unstructured sensitive data has not been discovered and classified, leading to potential data risk landmines and unknowns.
• Debilitating alert fatigue: Organizations are overwhelmed by DLP alerts, with 92% either deferred/left for inspection  after 24 hours or false positives/not remediated. 47% of DLP alerts that are inspected within 24 hours are false positive.
• Administrative burdens: 68% of companies manage multiple DLP policy sets across their IT environments with disparate, siloed tools.

Download the full report here.