Why Counterfeiters Move Faster Than Compliance
When COVID pushed global commerce online, counterfeiters didn’t just adapt; they industrialized. What began as scattered imitation has evolved into a fully automated economy of deception powered by AI.
That shift has left many brand-protection teams playing catch-up.
According to WTR 2025, artificial intelligence has become a double-edged sword. The same technology that helps identify fakes now helps criminals create them faster, smarter, and at scale.
The uncomfortable truth, though, is that the real gap for legitimate brands isn’t worrying about morals. It’s operational speed.
This article kicks off our AI-Ready Brand Protection Guide, a six-part series designed to help brands rewrite their pre-AI playbooks into flexible, data-driven systems. In Part 1, we’ll explore the speed gap that defines modern brand protection, and what it takes to close it.
From Pandemic to Automation: COVID-19’s Lasting Impact
As physical stores closed, both brand owners and counterfeiters moved online. The only distinction was the speed at which counterfeiters were able to ramp up.
As Pymnts 2025 reported, platforms like Walmart Marketplace experienced a rise in scam postings.
Then came AI and ramped up brand abuse even further. It took that chaos and industrialized it, automating product uploads, image edits, and even review manipulation. Things that used to take days to set up manually began to take only hours.
Instead of a few bad actors, now we’re facing thousands of micro-stores, social commerce sellers, and short-lived domains that appear and vanish before traditional teams can blink.
It’s counterfeit-by-algorithm, and the scale is unlike anything we’ve seen before.
Counterfeiters use generative models to localize ad copy, spin up synthetic product photos, and relaunch entire storefronts overnight. Meanwhile, brands are still waiting for permission to experiment.
The New Asymmetry: Why Old Playbooks Fail
Many brand-protection tactics were developed for an era that was slower. Quarterly reviews of policies made sense when counterfeiters were manually uploading products. Now, as AI models update hourly (not annually), it is impossible to keep up with manual rules.
What used to be a predictable enforcement cycle has turned into a whack-a-mole marathon.
Traditional KPIs like number of takedowns create the illusion of progress, while the same sellers reappear under new identities. The result? Teams celebrate vanity metrics while real brand erosion continues quietly in the background.
To make things worse, regulations can’t keep up with model updates.
It’s not a lack of tech. It’s about timing, structure, and feedback. Inside organizations, bureaucracy drags the process, with too many signoffs and outdated rules. And without quick feedback loops, AI models can’t learn fast enough to stay ahead.
Closing the Speed Gap: Modern AI-Enhanced Protection Strategies
Speed created the divide, but learning will close it. The brands winning this next phase aren’t reacting but learning faster. The goal isn’t to match criminals click for click; it’s to build systems that adapt as quickly as the threats they face.
Let’s look at how leading brand-protection teams are turning AI from an abstract concept into a concrete advantage.
Internal AI Pilots
It’s a simple mantra to keep in mind: start small, but start now.
Implement a pilot project using artificial intelligence for either triage or clustering, coupled with light, flexible governance instead of long approval processes. Focus on the tools that deliver quick wins, like risk scoring and image/OCR similarity that spot counterfeit variations in seconds.
Also, smaller teams can start without enterprise budgets. There are open-source models and partner APIs that enable proof-of-concept work with minimal investment.
The real value is the experience from using these tools.
The sooner your team have the practical ability to test these tools with real data, the faster your organization will develop the instincts of how AI reacts in practice. When that happens, AI becomes a business partner that never gets tired, does not scroll and keeps your team focused on the investigations that matter most.
Cross-Functional Working Group
AI-driven threats don’t respect org charts, so your response can’t either. Bring Brand Protection, Legal, Marketing, and Risk into one fast-moving incident group that shares data and decisions in real time.
Forget the old email chains and reactive checklists.
This is about collective intelligence, not committee meetings. At this year’s INTA Anti-Counterfeiting Workshop, experts agreed that collaboration and shared data access are becoming the new standard for enforcement.
Platforms like Hubstream already make that collaboration practical, connecting evidence, communications, and case data in one place so teams can act faster and stay aligned when it matters most.
KPIs for the AI Era
If your dashboard still counts takedowns, you’re measuring the wrong thing. The focus now is on metrics that show whether the threat itself is shrinking, not just being pushed around, like time-to detect, cluster disruption rate, recurrence reduction and most importantly, the overall business impact.
Modern brand-protection teams must keep an eye on reduced consumer confusion, faster case closures, and measurable revenue protection. These outcomes earn executive trust and justify scaling investment in AI-enabled workflows.
To gain trust from the leadership team, transparency has become the new foundation. The more clearly you can demonstrate decision-making processes and data-driven support, the more credible your enforcement program becomes.
Final Thoughts: Use AI Safely While You Learn
Here’s the rule every brand-protection team should follow:
Don’t wait for AI to be perfect; use it safely while you learn.
So start small:
Run small pilots that automate your job to spot counterfeits faster.
Tighten collaboration between legal, brand, and risk teams so every incident response moves as one.
Redefine success metrics to measure speed, disruption, and business impact and not just takedowns.
Invest in tools that help you learn, so your system improves with every case instead of restarting from zero.
The truth is, AI won’t replace investigators. It just represents the foundation of a new defensive approach built on continuous improvement rather than hesitation.
In the next installment in this series will examine the “dupe economy” and how AI has transformed consumer confusion into a profitable business model.