As brand impersonation grows in scale and sophistication, security teams face a dual challenge: uncovering the full extent of the threat and deciding what to do with what they find.
For many, the first hurdle—detection—remains a work in progress. But for those with mature discovery pipelines, a new problem has emerged: volume. As visibility increases, so does the operational cost of reviewing and triaging what surfaces.
This creates a familiar operational bottleneck. While automated rules handle the straightforward cases, a long tail of ambiguous domains still requires human triage to determine whether they are safe, suspicious, or malicious enough to warrant takedown.
To address this, Red Sift Brand Trust is introducing the industry’s first AI Agent for lookalike classification, designed to replicate expert analyst review and bring scalable, high-confidence triage to the edge cases automation can’t resolve on its own.
Our new AI Agent pushes further, delivering analyst-level judgment on the grey areas rules can’t resolve and automating takedown at machine speed. We’re putting the same asymmetric advantage in defenders’ hands that attackers have enjoyed for too long.”
Rahul Powar
Co-founder and CEO, Red SiftWhy rule-based automation alone isn’t enough
Red Sift Brand Trust already automates the classification of most lookalikes using deterministic rules based on risk scoring, visual similarity, keyword presence, and infrastructure patterns. This filters out low-risk domains and allows teams to focus on more credible threats. But automation relies on clear thresholds and known signals. Its limits show in the grey area where intent is unclear and context is required.
Domains that contain logos or brand-aligned email addresses may belong to legitimate partners or may be crafted to deceive. Others with high visual or phonetic similarity might result from brand affinity or coincidence, or signal an emerging phishing campaign. Even with detection and scoring in place, security teams are often left to make the final judgment call.
Reviewing these edge cases, weighing partial indicators, and deciding whether to escalate, monitor, or ignore each domain consumes time and attention. At scale, this leads to backlog and operational risk—not from lack of visibility, but from the limits of manual triage.
Triage at analyst level, without the analyst
The Brand Trust AI Agent extends automation into the space where human judgment is still required, delivering expert-level triage for lookalikes that conventional rules can’t resolve.
We’ve built a multi-agent system in which each specialized sub-agent investigates potential impersonation threats from a distinct perspective, such as domain name similarity, visual content resemblance, verified business relationships, and other relevant signals. A central decision-making agent then reviews all sub-agent reports to deliver a final, high-confidence classification.
The AI Agent is designed to identify impersonations and lookalikes that are strong candidates for takedown, providing a structured rationale that helps teams understand and act on its recommendations quickly and consistently.
How it works
Brand Trust’s automation classifies the majority of lookalikes as either benign or lacking sufficient evidence for takedown. These domains are continually monitored in the background, and if additional malicious signals emerge, customers are alerted automatically. This removes the need to manually sift through large volumes of low-priority data, ensuring teams can focus on credible threats while the AI Agent handles ongoing triage at scale.
The remaining lookalikes are more complex—close enough to raise suspicion, but not definitive enough for a rules-based decision. These domains often exhibit high similarity, nuanced linguistic cues, or subtle overlaps in infrastructure. Some are surfaced by automation but lack enough evidence for confident classification, while others are manually flagged by users for further investigation.
This is where the AI Agent takes over. It applies contextual analysis across linguistic, visual, relational, and behavioral dimensions to classify these ambiguous cases with analyst-level precision—identifying takedown candidates, clearing false positives, and reducing uncertainty across the triage process.
Each domain is assigned one of three outcomes:
- Takedown candidate: Clear signals of impersonation or abuse, and has enough evidence for takedown.
- Impersonation: Clear signals of impersonation or abuse, but does not yet have enough evidence for a takedown. Brand Trust will continue to monitor it for evidence.
- Safe: Domain appears legitimate or benign, moved to low risk.
Let’s bring this to life with a few examples.
Avoiding false positives through context
A domain like redsoft.com may appear suspicious at first glance—it uses Red Sift’s logo and hosts a live webpage. Traditional systems might flag it as a high-risk impersonation. But the AI Agent sees what those systems can’t: context.
It analyzes the page layout, interprets visual signals, and understands that the logo appears in a social proof section, grouped with others. This suggests Red Sift is being referenced as a customer, not targeted for impersonation. It also cross-references known relationships and recognizes Red Soft as a legitimate partner.
With this fuller picture, the AI Agent confidently classifies the domain as safe—avoiding a false positive and saving analyst time.
Spotting silent threats before they launch
At the other end of the spectrum, a domain like immaculate-construction.com may appear harmless—it has no content and no live site. But the AI Agent notes its similarity to immaculatehomes.com, a known customer, and understands that the added keyword “construction” aligns with the brand’s business model.
Even without active content, the AI Agent identifies it as a potential spoof and recommends closer monitoring. If activity begins—such as phishing content or credential capture—the Agent will escalate it as a takedown candidate before it causes harm.
This level of analysis brings full classification coverage to every lookalike, reducing manual effort while improving consistency and confidence in response.
Built to get better
While the AI Agent is designed to operate autonomously, its performance improves over time through feedback from the security teams who rely on it. Every classification includes a summary explaining the decision, and customers have the ability to flag any recommendation they believe is incorrect.
These inputs are fed back to Red Sift’s AI Lab to inform ongoing model refinement, ensuring the AI Agent continues to adapt to new impersonation techniques, edge cases, and patterns of abuse observed across different industries and geographies.
By incorporating this feedback loop directly into the platform, Brand Trust ensures that automation does not come at the expense of accuracy and that human insight continues to shape the system’s ability to detect and act on threats with confidence. This feedback is used to improve performance over time, without ever exposing customer data—inputs remain private, anonymized, and never used to retrain models directly.
See the AI Agent in action
The AI Agent will be available on an opt-in basis, reflecting our commitment to supporting diverse organizational AI adoption policies.
To see how the AI Agent operates in practice—and how it can help your team reduce manual triage and act on lookalike threats with greater speed and clarity—request a demo to explore the experience.
