AI Addressing New Need to Secure Data from The Inside Out

Today’s traditional perimeter-based cybersecurity measures are increasingly inadequate due to threats that arise from within an organization. Whether born of malice or mistake, it’s clear that insider threats are only further bolstered by the fast-growing use of cloud computing and mobile devices as well as the rise of remote work. Our workplaces are now thoroughly blurred. The once-clear boundaries of organizational networks don’t exist, rendering conventional defenses far less effective.

Post-pandemic reports have consistently shown organizations consider their existing perimeter-based security models unable to properly secure remote employees, let alone in-office teams. Recent studies indicate that insider threats are the initiator of nearly half of security breaches, and in some cases cause millions of dollars of losses or related expenses.

To address this ever-escalating insider threat challenge, compliance standards such as NIST 800-171 and ISO 27001 emphasize the need for data-centric security approaches that protect information at its core, regardless of its location or access point. This translates into the need for security at the file level, providing organizations with true control of their data. By adopting AI-based intelligent content protection strategies, organizations are able to improve file-based security through understanding their content at a deeper level and identifying potentially sensitive content not otherwise noted.

The New Paradigm: Content-Centric Security

In the past, insider threat solutions employing User Behavior Analytics (UBA) have focused on broad activity tracking, such as network logins, application usage, social media activity, and other behavior that potentially creates an unending number of alerts that can easily become unmanageable and unhelpful. While well-intentioned, their methods typically fail to prioritize the data layer where the most critical corporate assets actually reside. This oversight leaves organizations vulnerable to subtle, unauthorized interactions with sensitive content – particularly by users with privileged access who may appear “normal” in terms of general behavior.

A much more effective approach must focus on the data itself. Modern UBA capabilities should be purpose-built to detect anomalous data access patterns, ensuring that deviations in file access, modification or sharing are appropriately flagged – regardless of the user’s role or clearance level. Such a system must operate at the data layer, monitoring abnormal interactions with files containing sensitive information such as Controlled Unclassified Information (CUI) or other regulated data within a target market.

The addition of dynamic content analysis and classification is essential in order to enhance this new data-centric approach. By examining the actual content of files in real time, insider threat solutions can automatically identify and classify sensitive data, even when it is improperly labeled or entirely unmarked. This capability ensures that organizations are not only detecting unauthorized access but are also proactively securing the content itself.

The Expanding Role of AI in Identifying and Protecting Sensitive Content

One of today’s most significant challenges is the identification of sensitive content within vast repositories of files that lack distinctive markings, titles, or metadata. Afterall, employees create and use files in their own way, on their own time and in ever-unique manners. Traditional methods of data classification often rely on predefined labels or manual tagging, which are prone to human error and can leave critical information unprotected. This is where AI plays a pivotal role and opens up vast capabilities at the front lines of content protection. AI can be used to analyze files, identifying sensitive content within those files which might require special treatment and control. The addition of AI-driven classification ensures that even unmarked or mislabeled files are properly identified and secured, reducing the risk of accidental exposure or deliberate exfiltration.

Preventing Data Exfiltration at the Source

Today’s comprehensive insider threat solutions must go beyond identifying and classifying sensitive content to actively preventing unauthorized transfer of files to non-corporate controlled locations. This involves integrating data exfiltration controls that leverage the insights from content analysis, AI-driven classification, and tagging to enforce customizable policies that protect sensitive files. By identifying sensitive content, whether explicitly labeled or detected through AI-powered analysis, modern solutions can proactively block unauthorized attempts to transfer such data to USB devices, cloud storage platforms, or email attachments.

For example, in real-world scenarios like intellectual property (IP) theft, a malicious insider might attempt to download proprietary designs to a USB drive or upload them to personal cloud storage. Similarly, during ransomware attacks, exfiltration of encrypted files often occurs as part of a “double extortion” scheme. Organizations must be able to monitor such activities in real time and uses automated policies to prevent unauthorized transfers before they happen, regardless of the user’s role or permissions. Enterprises can no longer depend on alerts that require human intervention to protect data. Sensitive information is likely already compromised by that point, making the solution reactive and ineffective.

When combined with automation, powerful AI-driven insider threat systems can enforce granular access, transfer, and usage controls at the file level, ensuring that sensitive content remains secure while still allowing authorized workflows to proceed uninterrupted – a key factor in positioning security as a business enabler rather than a cost. This proactive approach helps keep IT personnel working efficiently while also significantly reducing the risk of insider threats and data breaches by stopping data exfiltration at its source.

Through the unification of AI-driven content analysis, intelligent tagging, and real-time automation, this insider threat approach dramatically enhances content security while maintaining compliance with regulatory requirements that can otherwise create countless headaches. As insider threats grow more sophisticated, preventing data exfiltration at the source represents a critical pillar of an effective, future-ready insider threat defense strategy.

About the Author

Rob Sims is the CTO and Co-Founder of Alchemi Data Management. He is a seasoned technology leader and entrepreneur with over three decades of experience in data management, enterprise software and hardware solutions. As the Founder and Chief Technology Officer of Alchemi Data Management Inc., he drives innovation in data security, risk reduction, and content management, helping organizations maximize the value of their digital assets.

Prior to founding Alchemi, Rob served as President of CitadelTek for over a decade, where he developed cutting-edge enterprise tools that provide insight and knowledge about file content, regardless of where it is created or stored. His expertise in data risk and reward strategies has enabled businesses to enhance operational efficiency and information security. Previously, Rob was President & CEO of Crossroads Systems, leading the company for nearly nine years in developing high-performance data storage and protection solutions. His leadership played a pivotal role in bringing advanced enterprise products to market.

With a strong foundation in engineering, Rob has held roles in new product integration and failure analysis at Kentek and Exabyte, gaining deep technical expertise in hardware and quality assurance. Rob holds a Bachelor of Science in Electrical Engineering (BSEE) from Colorado State University and further honed his business acumen at Harvard Business School’s School of Management. Based in Round Rock, Texas, he continues to shape the future of data management through innovation and strategic leadership.

Rob can be reached online at [email protected] and at our company website https://alchemi-data.com/