“Alert fatigue” must be the most common malady among cybersecurity professionals. According to a recent survey, 56% of large companies handle 1,000+ alerts each day. For 70% of security professionals, the volume of alerts has doubled in the past few years, with more than 51% of campaigns involving some form of AI-generated brand spoofing.
For most companies, the concern isn’t simply the volume—it’s the time lost chasing notifications that turn out to be non-issues. Each false positive pulls your team away from real threats that are harming your business and your brand. The stakes could not be higher. Last year, brand spoofing cost companies nearly $3B in consumer revenue, and every hour an attack continues, it grows stronger, spreads further and becomes more expensive and time-consuming to eliminate.
The answer to the alert fatigue and noise in the system? Narrow AI models that can sift through billions of data points to pinpoint high-risk lookalike domains lurking in the noise.
My aim is to teach you how to use AI-driven brand protection to monitor massive domain datasets and stream AI-vetted alerts directly into your Security Operations Center (SOC) for swift action. The goal? Help you defend your brand, save your business money and maybe even save your own sanity.
Where to start?
This is the eternal question among security practitioners. With an inbox full of alerts and your team pinging you every other minute via MS Teams or Slack, getting to the 2-5% actual threats (signal) and discarding the remaining 95-98% (noise) gets you to the most critical threats to your business.
Detect
You’re not going to get far without good detection. Your path needs to begin with finding the unknowns that could create fraud and liability that impacts your brand. Strapped security teams don’t have time to be thinking of lookalikes and tracking them in spreadsheets. And they also don’t have time to be sorting through harmless lookalikes or shadow IT. Best-in-class detection begins with scanning the internet daily for the 100+ million newly activated domains, subdomains and hostnames that could impact your brand.
But there is more that can be offloaded! There is all sorts of metadata for a given domain or host that can be used to discover and assess previously unknown assets. This can include evaluating things like WHOIS, SSL, mail records, and other sources for a given domain. It is a ton of work to do this manually – but the right tool can do the heavy lifting for you.
While a lot of practitioners get hung up on feature-sets, I instead emphasize that brand stakeholders should focus on results. With detection, the goal is mapping your digital estate without the need for manual, lengthy review. Detection not only cuts through the noise to save time and money, it also stops liability and fraud in real time by detecting imposter sites before they’re weaponized. And as an added bonus, it can also help you discover forgotten and unknown assets.
Analyze
If detection allows you to see out into all the internet quickly, analysis refines your initial results with AI models that detect logos, faces, page content, and industry classification, giving you the context to identify and prioritize malicious sites.
For teams relying on manual workflows, reviewing a handful of sites could be an all day affair. But for security teams, narrowly trained AI can eliminate these mundane tasks so practitioners can focus on more important things. No longer do analysts have to evaluate a lookalike website for logo misuse. Instead they can focus on more important things.
Doing something useful with the risk signals is even more important. For instance, what is the difference between a site for a business that resembles your own and one that has been set up with criminal intent? AI can tell the difference with intelligent risk scoring, dynamically assigning a score based on the level of impersonation, industry categorization and the financial impact of the campaign.
Since every organization has its own risk parameters, a one-size-fits-all approach is not going to work. A site harvesting personal information for a real estate firm is concerning. But for a healthcare provider, with the sensitivity around patient data, it is a full-on emergency.
Again, as with detection, the goal remains to cut through the noise as quickly as possible to project brand integrity to foster trust and loyalty with your customers. With more time, speed of action can reduce any potential damage to your organization.
Enforce
Enforcement is where things get serious. Having done the initial detection and assessment, narrow AI models can filter out the roughly 80% of domains found to be benign. For the remaining 20%, AI agents can assess the context of each potentially risky site with the deep intelligence of a human analyst.
The goal here is to replace manual threat review with a process that detects critical issues you might have otherwise overlooked and triages them for the quickest possible resolution. For even greater efficiency, you can integrate your workflow with Security Operations Centers (SOCs) and Security Information and Event Management (SIEM) team, so preliminary enforcement is initiated as soon as possible.
As an example of what I’m talking about, “redsoft.com” appears suspicious—it uses Red Sift’s logo and hosts a live site. Traditional systems might flag it as malicious. Thankfully, the AI Agent recognizes the logo in a “social proof” section, cross-references business relationships and identifies “Red Soft” as legitimate. It classifies the domain as safe, avoiding false positives and saving the analyst’s time.
This is a simple example, I realize, but it demonstrates how agentic AI is not a future technology, but a practical tool in the here-and-now.
Scaling for the future
The noise is not going to stop. Every day, bad actors are innovating new ways to attack your brand. The good news,when properly deployed by companies, AI can defeat that asymmetry. In lieu of the false positives of lesser solutions, the solution that cuts through the noise is the one that gives you triage at the analyst level, without the analyst.
Whether you choose to automate one or two steps or the entire process, the AI agent in your brand protection solution extends automation into the grey area where human judgment is needed, delivering analyst-level triage for lookalikes rules can’t resolve.
The bottom line is that whether you are a billion-dollar brand or a local business, brand spoofing is a $3 billion-dollar-a-year problem you have to deal with. No one is too big or too small to be a target. In fact, I’d go as far to say those with a smaller budget are far more likely to be at risk, given the expectation of no brand protection tool in place.
For additional guidance on how to manage this situation, dive into the latest strategies in the Brand Trust Resource Center.
Rahul Powar is co-founder and CEO of Red Sift. Previously, he was Head of Advanced Products at Thomson Reuters.
