Using AI to identify, expose online drug counterfeiters

How drug counterfeiters exploit AI

Once limited to simple packaging replicas and small-scale distribution methods, counterfeiters have upgraded their fraudulent tactics by combining AI-powered technology with the rise of popular e-commerce platforms.

"They are using AI to replicate pharmaceutical packaging, set up convincing fake websites and manipulate search algorithms to target vulnerable patients," Lee explained.

With limited visibility across the global supply chain, pharmaceutical companies struggle to trace products end-to-end, leaving blind spots that counterfeiters are quick to exploit through gray markets, digital storefronts and other online platforms.

"Anyone can set up a fake website selling counterfeit medicines using stolen brand assets -- AI tools make this easier than ever," he pointed out.

Social media has become fertile ground for advertising and marketing counterfeit drugs, where fake accounts will post images with little or no text to bypass keyword-based monitoring systems. By the time a manual reviewer catches on, initiated transactions might have already migrated off the platform.

How AI is exposing counterfeits

Through various AI techniques, including machine learning and deep learning, AI-integrated models are exposing counterfeit activity at a scale unmatched by manual monitoring using pattern recognition, behavioral analytics, network mapping, image analysis, and natural language processing.

Lee recalled a time when MarqVision analyzed suspicious Facebook activity for one of the world's largest pharmaceutical companies. What began as a single flagged account quickly expanded into a larger counterfeit network.

"Being able to accurately detect a single suspicious account on social media often revealed a broader network of related profiles," he continued, revealing a pattern found by MarqVision's analysis. "Illegal seller networks tend to cluster around specific product categories."

Take, for example, social media accounts selling inauthentic oncology drugs -- these profiles are often connected with other sellers trafficking similar products.

"This pattern highlights that both product lines and therapeutic areas shape how these networks form and operate," Lee suggested. "By analyzing an account's broader network, including connections, shared content and behavioral patterns, we can surface additional unauthorized sellers operating within the same vertical and take action fast."

Unlike traditional monitoring methods, which struggle to keep up with the speed and volume of counterfeit listings, this form of network mapping allows pharma companies to see how sellers operate in interconnected webs.

Seeing these clusters enables pharma companies to focus enforcement efforts on areas with the highest financial and reputational risks. For instance, products associated with high costs and strong demand, such as oncology, rare disease and specialty medicines, are especially appealing to counterfeiters and therefore need stricter monitoring.

"AI can now scan hundreds of online marketplaces and popular social media channels to detect brand violations in real time and initiate takedowns before counterfeit products reach consumers," Lee described.

AI systems that integrate computer vision, natural language processing and behavior analytics can detect even subtle differences in packaging, listing language and seller behavior, enabling pharmaceutical companies to oversee and intervene globally much more effectively than manual processes could ever allow. This approach marks a departure from slow, reactive enforcement toward proactive, technology-driven brand protection.

"AI is revolutionizing intellectual property protection by automating what used to take weeks or months," Lee emphasized.

Strategic priorities

In the effort to combat drug counterfeiters using AI, the following strategic priorities have become industry recommendations:

• Global surveillance. Use AI tools to simultaneously monitor e-commerce, social media and dark web forums to provide visibility across opaque supply chains.
• Predictive analytics. Use advanced analytics to spot fakes as well as forecast risks by geography, product line and therapeutic area.
• Authentication innovation. Add consumer-facing protection that reinforces trust in brand authenticity by adding smart labels, serialized barcodes and mobile verification tools to products.
• Collaborative intelligence. Share necessary anonymized data with peers, regulators and associated platforms to strengthen defenses against counterfeiters that operate in networks.
• Integrated strategy. Make brand protection the common thread of commercialization, market access and long-term growth plans.

With the use of AI, the drug diversion and counterfeit market is only growing more advanced. But AI-driven monitoring and predictive analytics tools are proving that this technology can be used against itself and keep pace.

"Over the next decade, we'll see AI models capable of real-time, cross-platform enforcement that can learn and adapt as fast as counterfeiters evolve," Lee predicted. "With the right partnerships, technology can help close the security gap and protect vulnerable populations more effectively."

Alivia Kaylor is a scientist and the senior site editor of Pharma Life Sciences.