GUEST ESSAY: Robust data management can prevent theft, guard intellectual property

By Clark Frogley

In an era of global economic uncertainty, fraud levels tend to surge, bringing to light the critical issue of intellectual property (IP) theft.

Related: Neutralizing insider threats

This pervasive problem extends beyond traditional notions of fraud, encompassing both insider threats and external risks arising from partnerships, competitors, and poor IP management. Organizations dedicate substantial resources to detecting and preventing fraudulent activity in customer accounts.

Yet, the rise of internal fraud presents a unique challenge. Perpetrated by insiders who already possess unrestricted access to highly sensitive data and systems, internal fraud not only defies easy prevention but also imposes substantial costs.

Annually, American businesses suffer losses exceeding $50 billion, underscoring the impact on competitiveness in today’s fiercely competitive landscape. To navigate this complex landscape, business leaders must strike a delicate balance between fostering open research environments and securing their valuable IP, safeguarding both their business and innovative endeavors.

Remote work factor

The growing trend of remote or hybrid working has particularly intensified the issue, enabling trusted insiders to mask fraudulent activity by operating outside the conventional security perimeter. And while many instances of this type of fraudulent activity may start out as an accidental mistake, the longer the fraudster goes unnoticed, the greater the risk of an easy payout snowballing into more malicious actions becomes.

In some cases, insiders with malintent attempt to circumvent internal processes and policies by stealing innovation through a variety of methods, including gathering human intelligence from other employees and contractors, conducting digital and even physical surveillance operations, among other strategies.


Some insiders may borrow tactics from more traditional state sponsored intelligence organizations such as confidential information collection through practices like “ratting” — where cybercriminals utilize malware to access sensitive information. Another example of on-the-ground tactics includes Intelligence agencies exploiting graduate students at research universities to access sensitive materials and coercing professionals working on sensitive technologies to engage in activities like IP theft.

Organizations must prioritize data and decision intelligence to tackle these threats effectively. However, fragmented and siloed data pose a significant hurdle for businesses in mitigating these risks, hindering their comprehensive understanding of the risk landscape. The combination of mounting pressures, accelerated decision-making, and the rapid availability and volume of data has intensified the difficulty of maintaining an efficient and resilient IP protection environment.

Role of AI

One technology businesses are looking to detect and prevent fraud, waste, and abuse is Decision Intelligence (DI), which allows companies to connect data and identify patterns or anomalies that potentially indicate the kind of behavior that may probe an investigation. By leveraging advanced analytics and AI, it offers enhanced scrutiny of individuals and organizations, monitoring their vulnerability to risks from sanctioned or risky entities that jeopardize intellectual property.

To accomplish this, the broader Decision Intelligence strategy should encompass the integration of techniques like graph analytics and entity resolution.

Organizations have access to ample data; the key lies in adopting suitable technology to extract its value. Gartner predicts that by 2026, organizations that prioritize AI transparency, trust, and security will witness a 50% boost in adoption, business goals, and user acceptance of their models. This emphasizes the transformative potential of Decision Intelligence (DI) for organizations that aim to be prepared for disruptions and resilient in the face of challenges. One example of where this impact can come from is entity resolution.

Entity resolution, powered by advanced AI and machine learning models, efficiently connects, organizes, and analyzes data to accurately identify similar entities. It groups related records, establishing a collection of characteristics and labeled connections for each entity. Unlike traditional record-to-record matching in MDM systems, entity resolution enables organizations to introduce new entity nodes that play a crucial role in linking real-world data.

Reusable resource 

With a strong data foundation, businesses can leverage a dependable and reusable resource to automate and enhance decision-making organization-wide, addressing diverse challenges beyond IP theft detection.

A strong data management strategy is vital for companies to monitor illicit and unlawful activities, safeguard intellectual property, and stay competitive. It is crucial to have visibility into networks across different environments, whether it’s an advanced persistent threat, cyber threat, or supply chain issue. The key lies in connecting data to gain a comprehensive understanding and effectively address complex challenges.

Tackling IP theft is an ongoing and intricate challenge that necessitates sustained cooperation between businesses leaders, workers and stakeholders. Ultimately, to drive global technology innovation, businesses must turn to Decision Intelligence to reduce manual work and make quick, well-informed decisions to protect their intellectual property.

About the essayist: Clark Frogley is Head of Financial Crime Solutions at Quantexa. He began his career with the FBI investigating organized and financial crime and served as the Assistant Legal Attaché in the US Embassy in Japan. Previously, Frogley worked as an executive at IBM in positions as the global head of AML and Counter Fraud Services in Banking, the Financial Crime Practice Leader for IBM in Japan, and the Financial Crime Solution leader for AML, Sanctions and KYC.

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