This article was co-authored by Frances Hung, a summer associate in Reed Smith's San Francisco office. 

Parts I and II of this series examined how AI-generated hallucinations could create trademark liability: first through the Britannica v. Perplexity litigation targeting misattribution in retrieval-augmented outputs, and then through the Britannica v. OpenAI complaint advancing a Lanham Act theory against branded AI-generated content. Part III shifts to the operational question: What should your company be doing right now to protect its brands from misrepresentation in AI outputs? 

Why now?

AI systems are increasingly supplanting traditional search engines as the primary interface through which consumers encounter brand information. When users query an AI about a company, product, or brand, the system generates a response that may include characterizations of what the brand is known for, qualitative assessments, and references to competitors—all presented without directing the user to verifiable source pages. In June 2026, a digital marketing agency released a comprehensive study demonstrating that major AI engines systematically misattributed, hallucinated, or omitted brand information across multiple platforms.

Brand owners should supplement existing monitoring with auditing of AI-generated outputs as these platforms become the primary lens through which consumers encounter brands.

How legacy digital footprints can corrupt AI training data

The June 2026 study provides an instructive example of how legacy content amplifies brand misrepresentation risk. A reseller blog post approximately fifteen years old supplied performance figures the brand had retired years ago, yet AI systems presented this information as current. Similarly, a promotional code remained live on an independently registered reseller domain thirty-five days past its published expiration - exactly the type of stale content AI systems could resurface as authoritative.

AI systems rely on inferred consensus rather than real-time verification, meaning that outdated information persists as long as it remains accessible. Brands that fail to clean their digital publishing ecosystem (including legacy reseller pages, abandoned press releases, old directory listings, and discontinued product pages) provide corrupted inputs that AI models absorb and present as authoritative. Unlike conventional search, where users click through to source pages, AI systems can combine and rewrite information from multiple sources into a single response, a process that introduces substantial risk of inaccuracy despite projecting confidence.

A practical framework for auditing AI outputs

The following five-phase playbook provides a structured approach for brand protection teams to detect, document, and remediate AI-generated brand misrepresentation.

Phase 1: Inventory and Discovery

The first step is mapping your brand’s existing digital publishing ecosystem, including all legacy content that may be feeding AI training data. This means crawling your corporate website, editorial blog, technical library, retailer and directory listings, all authorized agent domains, and all independently registered reseller domains. The audit should extend to third-party sites that AI engines cite as sources, because these sites form the input layer from which AI systems construct brand narratives.

The inventory should identify legacy content: pages that are outdated, contain retired product claims, display expired promotional codes, or reference discontinued partnerships. Think of sources where someone other than your official site represents the brand: authorized resellers and dealers, marketplace and platform storefronts, affiliates, franchisees, distributors, directory listings, and user-generated content. Each of these surfaces is a potential source of AI inputs.

Phase 2: AI Prompt Testing Protocol

Once you know what is out there, you need to know what AI systems are doing with it. Systematic querying of major LLM platforms should encompass curated queries about product features, pricing, promotions, competitive positioning, brand ownership, and historical claims. Testing should be repeated over time to assess reproducibility. Teams should test across all major platforms because performance varies dramatically: the same prompts that produce trademark misattribution on one platform may be answered correctly by others.

Phase 3: Documentation and Benchmarking

Log all AI outputs with timestamps, platform identifiers, exact prompts used, and complete response text. Create a rubric to score responses across relevant dimensions relevant to the brand, including naming frequency, citation rate (how often the AI cites the brand’s own domain), claim fidelity (whether marketing claims are accurately reproduced), and error classification (misattribution, genericization, fabrication, or omission).

Phase 4: Content Remediation

Remediation strategies should address both the brand’s own publishing ecosystem and any broader reseller network. Within the controlled ecosystem, teams should remove or update legacy content containing retired claims, expired promotions, or discontinued product information. For independently registered reseller domains, the brand should request takedowns of outdated pages, update authorized source content, and, where contractual authority exists, require resellers to maintain current product information. 

The key insight is that brands cannot correct AI directly — they must correct what AI trusts. AI systems update their answers when stronger, clearer consensus emerges across trusted sources. Corrections must therefore be applied at the source level: directories, articles, listings, and authoritative pages. Because AI systems recognize patterns rather than intent, the goal is to reinforce accurate information consistently across trusted sources.

Phase 5: LLM Optimization Best Practices

Being proactive here will result in more accurate brand representation going forward. Based on emerging best practices, brands should:

• Make online content AI-readable: Implement structured data markup (FAQ Page, Product, Organization schema) to help AI systems parse brand information accurately.
• Add llm.txt compatibility: To better control how content is used in AI-generated answers, create a text file at the domain root to signal AI crawlers what to index. This is similar in function to a robots.txt file, which tells web crawlers not to index certain files.
• Maintain content freshness: AI platforms favor up-to-date content, and companies with high AI visibility update content quarterly.
• Write clear, factual content without hedging language: Clarity makes content more valuable to AI systems.
• Leverage digital PR and earned media: 82% of links cited by AI come from earned media sources, and approximately 25% of all AI citations come from journalism.
• Standardize digital branding: Ensure entity clarity (consistent name, location, and brand facts across all digital properties) to prevent AI systems from confusing the brand with competitors or generating hallucinated details.

Bottom line

Parts I and II of this series documented the litigation risks for AI-generated hallucinations about trademarks. But litigation is reactive: a lagging indicator of potential reputational harm. Brand owners should reduce risk proactively by taking control of the inputs AI systems rely on and establishing documentation that strengthens their enforcement posture if litigation becomes necessary.

Treat this as an ongoing practice, not a one-time audit. Map your ecosystem. Test the outputs. Document everything. Clean your digital footprint. Optimize for AI consumption. And repeat.