How AI Augments Brand Protection Software

Protecting your brand has always been a constant challenge, but the widespread adoption of AI has significantly magnified brand-targeted fraud, making counterfeiting, impersonation, and fraud faster, cheaper, and more convincing. The technology now enables criminals to generate professional-quality fake marketing materials in minutes rather than weeks, such as AI-generated advertisements that mimic company branding and redirect customers to fraudulent sites. AI also makes it easier to produce and sell sophisticated knockoffs that damage both revenue and brand credibility.

Fraudsters are now routinely using AI to create elaborate impersonation schemes, including fake celebrity endorsements, manipulated influencer videos, and convincing business communications that appear to come from company executives. 

What makes these threats particularly concerning is their evolving sophistication. Traditional fraud indicators like poor grammar or obvious errors are disappearing as AI produces increasingly polished content, and security experts see their use in advanced personalization tactics targeting trusted relationships.

The rapid pace of AI advancement means companies are in an arms race to develop more robust measures to counter these mounting threats to their revenue and reputation.

So how can you best defend your brand against the rising threats of impersonation, counterfeiting, and unauthorized use of your intellectual property? 

Fortunately, the same AI technologies that empower criminals can also provide organizations with superior defensive tools. Brand protection software monitors millions of data points simultaneously, and with AI it can sift through the noise to identify threats by understanding the context of the pages, and respond to threats at machine speed. However, despite the hype, AI by itself is no magic bullet and choosing the wrong solution can leave you complacent and vulnerable. For businesses facing the onslaught of AI-powered brand abuse, the question is no longer whether to adopt AI for brand protection, but how to do it correctly.

Read on to discover the right way to use AI to achieve unprecedented speed, accuracy, and scale for your brand protection efforts.

The Evolution of Brand Protection Challenges

Your IT and security teams go to great lengths to secure your own websites, apps, networks and other infrastructure, but to be successful in 2025, you must also engage your customers across numerous third-party digital platforms, such as social networking sites or eCommerce marketplaces. However, each new outpost opens another door to potential threats because the security of these spaces is mostly beyond your control, and many are magnets for bad actors who continuously refine their tactics to better exploit your brand and evade detection. 

Today, the availability of Generative AI (GenAI) makes brand abuse more accessible to a wider range of fraudsters. Both sophisticated adversaries like nation-states—that have access to advanced attack techniques—and less capable criminals can easily leverage these tools to scale and optimize their operations. For example, Large Language Models (LLMs) simplify impersonation and spear phishing attacks by enabling novice scammers to take just a few samples of a target's writing and craft personalized, believable lures with minimal effort.

Some of the most common AI-enabled cyber threats to be aware of include:

• Impersonation attacks on social media platforms use AI to better imitate both brands and executives. Fake profiles that look and sound authentic can trick customers, partners, and employees into executing fraudulent transactions or revealing sensitive information. And AI video advancements can help attackers create deepfake videos that push scams from what appear to be legitimate sources.
• Phishing campaigns become more credible when AIs generate natural-sounding language that overcomes traditional red flags such as poor spelling or incorrect grammar.
• Typosquatting attacks become harder to spot with the human eye, with AI-driven similarity analysis that selects domain names visually indistinguishable from legitimate ones, including using unicode homograph techniques.
• Counterfeiters use AI to manufacture and market convincing fakes, using stolen logos and brand imagery to scam consumers. These knockoffs not only hijack revenue but can damage brand reputation when customers receive low-quality or potentially dangerous goods.
• Deepfake and Synthetic Media Attacks are launched using generative AI to create realistic fake audio, video, or imagery impersonating key people. For example, an AI-generated voice mimicking a CEO that fraudulently authorizes fund transfers, tricking employees into taking the action.
• Trademark infringement aided by AI-generated content and deepfakes makes it increasingly difficult to distinguish between authentic and fraudulent brand communications.

Typically, threat actors will use several or all of these techniques in combination to exploit your brand, deceive your customers, and drain your revenue. 

Perhaps most concerning is the emergence of coordinated brand abuse campaigns. Rather than isolated incidents, sophisticated threat actors now orchestrate multichannel attacks that simultaneously target websites, social platforms, mobile apps, and messaging services with access to legitimate customers or employees. These multi-channel attacks make the scams look more realistic and make it harder for a company to take everything down quickly. By the time someone manually responds to each attack, it’s often too late and customers have already been scammed.

Traditional Brand Protection Approaches vs. AI Brand Protection Software

Digital threats can spring up and achieve global reach within minutes or hours, leaving traditional brand protection methods in the dust. The legacy approach of manually monitoring and reacting to incidents on specific channels creates dangerous blind spots, while reactive remedies leave brands perpetually one step behind their increasingly agile adversaries. The shortcomings of traditional brand protection become even more apparent when compared with AI-powered solutions:

Scope

Traditional approaches are limited to manual reviews of known platforms. This is a resource intensive approach that requires significant amounts of time and human labor, and is increasingly impractical in the context of just how much new digital content is produced every second. On the other hand, AI solutions offer  a better way to effectively scan through large data sets, better reducing the noise of false alerts. 

Reactive vs. Proactive

Traditional brand protection relies on exact matches to a URL or in text. This leaves you reacting to threats long after they start, giving bad actors plenty of time to dig in and profit off your company's reputation. However, AI systems allow for more contextual matching, such as matching to logo variations or the arrangement of a page. For example, a threat actor could save a page as a screenshot, working around typical DRP scanning techniques. An AI brand protection solution could analyze the image to find offending branding and brand names.

Detection-to-Response Timelines

Time to response can also differ significantly—where traditional approaches might take hours or days to progress from detection to response, AI-driven solutions enable near-immediate identification and automated takedown submissions to providers. 

How AI Works in Brand Protection Software

The most effective AI brand protection software leverages a broad range of technologies to rapidly identify and respond to developing threats against your online presence. These systems go far beyond simple keyword matching to intelligently analyze the context and intent behind potential risks, whether they appear as text, images, or even video.

This is possible because AI excels at pattern recognition across vast datasets, enabling the detection of the most subtle brand infringements. What's more, machine learning algorithms continuously improve over time, smoothly adapting to new threat vectors as soon as they emerge. 

ZeroFox's AI-powered platform exemplifies this approach, combining multiple advanced technologies to create a protective shield that scales with the expanding digital threat landscape. Let's take a look at its primary features:

Key Technologies Powering ZeroFox AI Brand Protection Software

• Machine learning models trained on legitimate and fraudulent examples improve detection accuracy over time.
• Large-scale data processing monitor billions of content sources continuously.
• Relationship mapping translates disparate threats into a comprehensive overview of attack patterns.
• Image recognition uses single-shot learning algorithms to identify unauthorized brand logo use and subtle visual similarities between legitimate and fraudulent content.
• Natural Language Processing (NLP) analyzes text to understand the context and sentiment behind potential threats.
• Optical Character Recognition (OCR) extracts alphanumeric text from images across web sources, uncovering threats that text-only analysis would miss.
• AI risk scoring helps reduce noise and false positives and assists analysts in focusing on relevant alerts for triage by identifying key indicators and cutting initial processing time.
• Video analysis looks for deep fakes or brand abuse by extracting and analyzing video frames to identify unverified content featuring brand images. ZeroFox also uses audio transcription to identify verbal brand threats.

However, despite the power and flexibility of AI, it's not the all-in-one solution it's often portrayed as. ZeroFox understands that it's only by leveraging AI technologies in partnership with human intelligence experts that you can reliably strengthen your defenses and stay ahead of threat actors.

The Human + AI Approach to Brand Protection

Many cybersecurity companies rely entirely on AI technologies to deliver their complete solution, meaning an AI analyzes a threat, makes a decision, and then pushes it directly to the customer. However, this level of automation can only be realized through compromise—these companies limit the scope of the data they analyze, focusing only on content that can be processed automatically. 

This strategy is the only way they can achieve the improved metrics they report, such as incredibly fast takedown times. We find that newer organizations will then use these metrics to quickly make a name for themselves in the space, sacrificing scope and quality. 

Senior ZeroFox Product Marketing Manager Matthew Levine highlights why this end-to-end AI design results in a weaker offering.

"Companies relying solely on automation miss plenty of things because the content doesn't fit into their algorithm box," he explains.

"Anything that's difficult or messy, they say: 'Let's just skip it'." 

He contrasts this with ZeroFox's more comprehensive approach: 

"We don't want to make our services less effective just to increase a metric. We want to get as much data in as possible so that we don't miss anything, because our main drive is to deliver the highest quality intelligence that we can."

To achieve this exceptional standard, ZeroFox also ensures seasoned professional analysts are always proactively involved in the workflow: AI handles repetitive tasks like monitoring, detection, and initial classification, while human analysts validate findings and review alerts before they reach customers.

"It's not just AI making blind decisions in a box," says Matthew.

Many other AI solutions are indeed "black boxes", meaning even their developers cannot explain how these systems make their decisions. However, modern AI platforms like those deployed by ZeroFox provide explainability features that help humans understand why specific threats were flagged and what indicators triggered detection.

"If there is any kind of inconsistency even beyond all the checks and balances that we do, an analyst can look at it and confirm that this is true and accurate," Matthew points out.

This "human + AI" model delivers results: AI delivers scale and speed, while humans contribute context and judgment to reduce false positives and ensure customers receive only alerts to genuine threats. But as we look towards the future, the bigger takeaway is that human feedback also enables continuous improvement to refine AI models. While LM tools continue to make leaps in capability, they are only as good as the information fed into them. 

That’s why ZeroFox knows that training our AI systems with over a decade of knowledge and findings, from expert analysts reviewing and triaging alerts, is imperative to success in the future of AI brand protection. Meanwhile, new upstart DRP providers have no data to reliably train their systems on. So, the question when choosing an AI brand protection solution is: Are you really willing to wait it out with subpar service as fully AI systems learn on the job, or should your organization trust established industry leaders with the scalability to handle even the most targeted companies?

As ZeroFox’s Director of Platform Experience notes, "You will always be stronger when you have a more traditional set of protection capabilities in combination with the newer, latest, and greatest kind of AI capabilities."

Let's take a closer look at what that means.

Key Benefits of AI-Powered Brand Protection

Organizations implementing hybrid AI + human brand protection solutions like ZeroFox gain significant advantages in several areas, including:

• Greater operational efficiency from AI-powered monitoring that reduces manual effort for security teams, prioritized alerts that focus attention on highest-risk threats, and automated takedown submissions with complete visibility and transparency every step of the way.
• Enhanced detection capabilities including comprehensive coverage that monitors the entire external attack surface, expert context-aware analysis that understands relationships between events and identifies campaign-level threats, and combined image and text analysis that detects threats across multiple content types.
• Improved threat intelligence features pattern recognition to identify emerging threat trends, predictive insights to anticipate potential vulnerabilities, and cross-industry visibility that takes advantage of insights from across the customer base.
• Faster response timelines result from real-time detection identifying threats as they emerge, automated workflows initiating response actions immediately, and the Global Disruption Network partnerships expediting takedowns.  One of ZeroFox's partnerships is with Google's Web Risk, which creates a warning system that allows content to be blocked from billions of devices within just 15 minutes, preventing many threats from reaching their intended target.

The bottom line for teams that choose an AI + human approach to brand protection? Operational efficiency that helps your team scale without compromising on breadth of detection, quality of intelligence, or brevity of timelines—no matter the size of your brand.

ZeroFox: Leading Innovation in AI-Powered Brand Protection

ZeroFox continues to push the boundaries of what's possible in brand protection with proprietary AI models developed specifically to handle the threats beyond your cybersecurity perimeter. Our comprehensive protection ecosystem addresses the full spectrum of external challenges, while full-lifecycle capabilities cover everything from detection through remediation and ongoing protection, and our commitment to continuous innovation ensures regular enhancements that keep you ahead of emerging threats.

ZeroFox's approach to brand protection encompasses:

Intelligence:

• Ongoing intelligence provides trend analysis to identify evolving threats and tactics.
• Cross-customer insights that leverage patterns seen across industries.
• Proactive recommendations that suggest defensive measures before threats materialize.

Monitoring:

• Social media platforms to identify impersonation, scams, and brand abuse.
• Domains and websites to detect phishing sites and brand-infringing domains.
• Dark web spaces to uncover planning, stolen credentials, and underground activities.
• Mobile app stores to find unauthorized or malicious brand-related applications.

Analysis:

• Intelligent alert triage includes contextual analysis to understand the severity and impact of potential threats.
• Risk-based prioritization focuses attention on highest-impact issues.
• Campaign correlation groups related activities for comprehensive response.

Disruption:

• The Global Disruption Network leverages partnerships for rapid enforcement.
• Streamlined workflows automate common response processes.

ZeroFox gives you access to industry-leading AI technology, expert analysis, and global enforcement capabilities to effectively maintain control of how and where your brand appears online and protect customers from scams and fraud.

Book a free demo to see what ZeroFox can do for your team.