This is the third article in our AI Agent series. In Part 1, we introduced Red Sift’s AI Agent for lookalike classification – an intelligent solution for handling the ambiguous cases that rule-based automation can’t confidently resolve, offering analyst-grade triage autonomously. In Part 2, we took readers behind the scenes to explore the engineering challenges and design decisions that enable the agent to reason, adapt, and operate reliably at scale. Now, with the feature in beta testing, Part 3 will focus on showcasing how well it performs when applied to real-world data.
A quick recap for new readers
Red Sift’s AI Agent for lookalike classification is an intelligent triage system designed to determine whether a suspicious domain has been intentionally crafted to mimic an official brand or if any resemblance is purely coincidental. It ingests multiple contextual signals – including domain names, DNS and WHOIS data, screenshots, and customer intelligence – and applies advanced reasoning to make a binary classification: impersonation or benign. Crucially, it doesn’t just return a label – it also generates a natural-language explanation, mirroring how a human analyst might justify their decision. The agent was shown to produce excellent results across Red Sift-owned testing accounts with benchmark datasets.
The figure above shows the agent’s assessment for lookalike domain paypalmanager.my against the asset domain paypal.com. It presents the binary classification result, an executive summary, and a detailed explanation of the reasoning behind the decision. In this article, we’ll walk through each element in the Analysis Details section – covering domain name, visual content and business relationship – to make the agent’s reasoning clearer through real-world scenarios of known brands. Through our beta testing, we can visualise likely challenges facing security teams and where Red Sift’s AI Agent can streamline the actions needed. The brands chosen were selected by our engineering team and do not reflect any customer endorsement at this stage.
The agent in action
Domain name analysis
When analyzing domain names, the agent looks at how closely a suspicious domain aligns with the legitimate asset domain. This starts with examining exact matches, which often signal the highest risk, and extends to partial matches, where context plays a critical role in distinguishing between coincidence and intent.
Exact match analysis
Exact domain matches are one of the strongest indicators of potential impersonation, but context is key to avoiding false positives. A domain name alone doesn’t tell the full story – some matches may point to well-known brands, while others may simply involve common words that coincidentally overlap. To address this, the agent doesn’t just flag an exact match; it analyzes the nature of the matched term to determine whether it represents a distinctive brand identity or a generic term. By reasoning through this context, the system can distinguish between deliberate mimicry and accidental similarity, allowing for far more precise classification.
Lookalike: openai.xyz
Asset: openai.com
Domain analysis: The lookalike domain ‘openai.xyz’ is an exact match to the asset domain ‘openai.com’ in terms of the brand name, differing only by the top-level domain, which is a common tactic in lookalike domains.
Lookalike: grape.net
Asset: grape.com
Domain analysis: The domain names share the same dictionary word ‘grape’ but differ only in TLD, and since ‘grape’ is not a recognized brand, the domains are classified as unrelated rather than confusable.
Lookalike: apple.vip
Asset: apple.com
Domain analysis: The lookalike domain ‘apple.vip’ is an exact match to the brand name ‘apple’ used in ‘apple.com’, differing only by the TLD ‘.vip’, which can cause confusion due to the strong brand recognition of Apple.
Partial match and contextual analysis
A domain doesn’t have to be an exact match to raise suspicion. Even partial matches – where the brand name appears within a longer domain – can signal potential impersonation. In these cases, the agent evaluates the surrounding context of the brand term, analyzing whether the additional words strengthen the likelihood of a deliberate attempt to mimic or indicate a coincidental use. This nuanced reasoning helps separate truly risky domains from those that merely contain similar wording.
Lookalike: merrill-n-sons.com
Asset: merrill.com
Domain analysis: The lookalike domain includes the exact brand name ‘merrill’ but adds a generic suffix ‘-n-sons’ that does not relate to Merrill’s financial services, categorizing it as irrelevant brand inclusion.
Lookalike: merrill-securelogin.gb.net
Asset: merrill.com
Domain analysis: The lookalike domain includes the exact brand name ‘merrill’ from the asset domain and adds ‘securelogin’, a term highly relevant to financial services, making it a relevant brand inclusion that could confuse users.
Lookalike: merrillinvest.com
Asset: merrill.com
Domain analysis: The lookalike domain ‘merrillinvest.com’ contains the exact brand name ‘merrill’ from the asset domain and appends ‘invest’, a term directly related to Merrill’s investment services, making it highly confusable.
Visual content analysis
Another important signal the agent examines is the content of the lookalike website. While domain names provide strong initial clues, they don’t always tell the full story. By analyzing on-page elements, the agent can validate or challenge the initial suspicion raised during domain name analysis. It inspects factors such as logos, visual layout, page structure, and key textual elements that may mimic the legitimate brand. This additional layer of scrutiny helps determine whether the site’s presentation reinforces the impersonation signal or suggests a benign, unrelated use, ultimately making the classification more accurate and context-aware.
Irrelevant content
In some cases, the domain name may appear related, but the website’s actual content tells a completely different story. These examples show how content analysis helps disqualify false positives by identifying sites that are legitimate but unrelated to the brand in question.
Lookalike: aa.co.nz
Asset: aa.com
Domain analysis: The domain ‘aa.co.nz’ shares the same two-letter string ‘aa’ as ‘aa.com’, but since ‘aa’ is not a proper brand name and the TLD ‘.co.nz’ indicates a New Zealand commercial entity unrelated to American Airlines, the domain name similarity is minimal and not indicative of impersonation.
Content analysis: The visual and functional content of ‘aa.co.nz’ clearly represents the New Zealand Automobile Association, with distinct branding, services, and industry focus from ‘aa.com’, which is American Airlines. Due to the different industries and branding, content similarity analysis confirms low similarity and no impersonation.
Lookalike: bankofamerica.me
Asset: bankofamerica.com
Domain analysis: The lookalike domain ‘bankofamerica.me’ is an exact match to the asset domain ‘bankofamerica.com’ except for the TLD, which can be confusing.
Content analysis: The lookalike domain hosts a personal blog unrelated to banking, with no similar logos or financial services, making content similarity low and not indicative of impersonation.
Same industry
Sometimes, a domain belongs to a business in the same industry as the legitimate brand, creating surface-level similarity without necessarily indicating impersonation. These scenarios highlight how content analysis can differentiate between competitive or generic industry presence and actual attempts to mimic a brand.
Lookalike: lifeinsurance.com
Asset: mylifeinsurance.com
Domain analysis: The lookalike domain ‘lifeinsurance.com’ is a generic industry term closely related to the asset domain ‘mylifeinsurance.com’, which itself does not contain a unique brand name, making the domains unrelated in terms of brand identity.
Content analysis: Both websites are professional and operate in the life insurance industry, but they have distinct branding and visual elements; content similarity is due to the shared industry focus, so content similarity analysis for impersonation is not applicable.
Lookalike: avivaforexinvest.com
Asset: aviva.com
Domain analysis: The lookalike domain includes the exact brand name ‘aviva’ plus relevant financial terms, making it confusable with the asset domain and suggesting an intent to leverage Aviva’s brand identity.
Content analysis: The lookalike domain uses Aviva’s name and similar branding in the same industry, strongly suggesting an intent to impersonate or mislead users. The logo isn’t an exact copy, but the overall style and context make the site appear affiliated with Aviva.
Lookalike: paypalloginin-usa.blogspot.cl
Asset: paypal.com
Domain analysis: The lookalike domain includes the exact brand name ‘paypal’ plus ‘loginin’ and ‘usa’, hosted on an unrelated free blogging platform, indicating a relevant brand inclusion but no official affiliation.
Content analysis: The site visually and functionally mimics PayPal’s official website with similar logos, colors, and messaging, creating a high trademark similarity that suggests an intent to impersonate rather than inform.
Different industry
A domain may share thematic or generic language with a brand without being part of the same sector. In these situations, content analysis helps clarify that the overlap is contextual, not deceptive, showing how industry differences can reduce impersonation risk.
Lookalike: insurance-comparison.com
Asset: aviva.com
Domain analysis: The lookalike domain does not contain the Aviva brand name or any confusing variant, instead using generic insurance-related words.
Content analysis: The site provides informational content comparing insurance providers, including Aviva, using logos fairly within articles, and does not mimic the official Aviva site’s branding or services.
Inactive domain
Not all suspicious domains are active websites. Many are inactive or parked, offering little or no content to analyze. However, inactivity doesn’t mean harmlessness—such domains can be held for future weaponization or used in later impersonation campaigns. Several customers find it valuable to surface these domains early, giving their security teams greater situational awareness and time to prepare before any malicious activity begins.
Lookalike: merrill-securelogin.gb.net
Asset: merrill.com
Domain analysis: The lookalike domain includes the exact brand name ‘merrill’ from the asset domain and adds ‘securelogin’, a term highly relevant to financial services, making it a relevant brand inclusion that could confuse users.Content analysis: The lookalike domain hosts a generic domain parking page unrelated to Merrill or financial services, showing no active impersonation or branding similarity, so content similarity analysis is not applicable.
Business relationship
When suspicious similarities are detected, domain and content signals alone may not provide the full picture. In these cases, the agent takes the extra step of examining the underlying business relationship between the lookalike and the legitimate asset domain. This deeper investigation helps distinguish between sophisticated impersonation attempts and legitimate domains that are actually affiliated with the brand. To do this, the agent gathers and cross-references information from multiple trusted sources across the internet. In the following examples, we’ll look at two different types of relationships the agent can identify, showing how this analysis adds another layer of precision to the classification.
Partnership
Lookalike: benadryl.com.ar
Asset: benadryl.com
Business relationship deep analysis:
The benadryl.com global site and Kenvue’s corporate brand pages identify BENADRYL as a Kenvue brand, indicating that the asset domain is operated directly by Kenvue. In contrast, the benadryl.com.ar site clearly identifies Laboratorio Elea Phoenix S.A. as the operator in its footer and product pages, providing localized product and company information specific to Argentina.
Additionally, Argentine pharmacy listings show BENADRYL products marketed under ELEA as the manufacturer, supporting the existence of a local licensing or distribution arrangement rather than direct Kenvue ownership.
Conclusion: The domains benadryl.com and benadryl.com.ar reflect a regional/local licensing or distribution relationship, not common ownership. The branding is authorized and does not indicate impersonation.
Ownership
Lookalike: nicorettecontest.ca
Asset: nicorette.co.uk
Business relationship deep analysis:
The nicorettecontest.ca site displays official Nicorette contest branding and includes legal language naming Kenvue Canada Inc. as the sponsor/organizer in its contest rules. This aligns with multiple public contest listings (e.g., InfiniteSweeps, Contest Scoop) that reference the same domain as the source for an official Nicorette NHL giveaway. Nicorette is a Kenvue brand, and Kenvue’s corporate materials and Nicorette Canada promo pages confirm this affiliation.
Meanwhile, nicorette.co.uk explicitly states in its footer that it is “published by Kenvue UK Limited,” identifying the same parent corporate group. The use of consistent branding, explicit legal disclosures, and public contest references establishes that both domains are part of Kenvue’s official brand ecosystem.
Conclusion: The evidence shows that nicorette.co.uk and nicorettecontest.ca are both operated by Kenvue entities. This constitutes a confirmed ownership relationship, not impersonation.
Results from beta testing
To understand how well the AI Agent performs in realistic operational conditions, we launched a beta testing program with around 50 participating accounts, representing a diverse set of industries, threat profiles, and traffic volumes. Over a two-week period, we collected early performance data to assess both the breadth of detection and the depth of reasoning the agent can provide.
83.8 suspicious domains per top 10 accounts (on average)
This number highlights just how prolific Brand Trust and the agent are at surfacing potentially risky domains—many of which may have previously gone unnoticed.
27.2 subtle suspicious domains without strong trademark infringement (on average)
These are cases where no obvious brand elements (such as logos) were detected, demonstrating the agent’s ability to identify nuanced impersonation attempts beyond straightforward copycat behavior.
10.4 legitimate domains with trademark fair use (on average)
These are domains where logos or other brand signals were used legitimately—for example, through partnerships, integrations, or product comparisons. This shows the agent can use context to separate malicious intent from legitimate usage.
Final thoughts
These early results show that the AI Agent doesn’t just surface more suspicious domains — it surfaces them earlier and with richer context. By clearly distinguishing between impersonation, subtle misuse, and legitimate activity, the agent helps security teams focus their attention where it matters most, cutting down unnecessary triage and improving overall threat visibility. This balance of scale and precision is what makes the beta outcomes so encouraging for operational teams.
Beyond detection, the agent also streamlines one of the most time-consuming parts of the workflow: reviewing ambiguous cases. By automating this layer of reasoning, it frees analysts from repetitive manual checks and enables faster, more consistent decisions, turning a traditionally resource-heavy process into an efficient and reliable one.
If you want to see how this capability can sharpen your team’s visibility and speed up response, request a demo to experience the AI Agent in action.
