How is the threat landscape evolving beyond simple phishing?

The retail sector approaches the 2025 peak holiday season facing a perfect storm. We are no longer contending with opportunistic human fraudsters or rudimentary scripts. We face a tidal wave of autonomous, generative AI-powered agents capable of mimicking human behavior.

According to Ran Arad, a subject matter expert at Memcyco, we must view phishing, digital impersonation, and account takeover (ATO) as an interrelated lifecycle.

Usually, a phishing attack provides the link to an impersonating site. The ultimate goal is rarely just to deface a brand. It is to take over the user’s account. This complete view requires a defense that understands the connection between the fake site and the stolen credential.

Attackers are moving beyond basic “typosquatting” (e.g., changing an ‘o’ to a ‘u’ in a URL). Today, we see sophisticated obfuscation using Unicode characters and Cyrillic alphabets. A letter ‘a’ in Cyrillic may look identical to a Latin ‘a’ to the human eye, but it registers as a different code. This allows attackers to create “perfect clones” that bypass standard visual inspections, tricking even vigilant holiday shoppers.

Why does the ‘scan and takedown’ model leave a ‘window of exposure’?

The dominant paradigm in the industry – often referred to as brand protection – operates on a reactive cycle called “scan and takedown.”

This approach involves scanning the internet for problematic sites using visual comparison, identifying a threat, and initiating a takedown request. While companies in this space add automation, the core mechanism remains semi-manual. You must identify the registrar, prove the violation, and wait for them to act.

This process creates a critical vulnerability gap that Arad defines as the “window of exposure.”

This window is the time elapsed between the moment a fake site goes live and the moment it is successfully taken down. In many cases, this process averages one week. During a high-velocity period like the “Turkey 5” (Thanksgiving to Cyber Monday), a week is an eternity. By the time the site is removed, the attack has happened, credentials have been harvested, and the damage is done. Relying only on takedowns leaves customers exposed during the attack’s most lethal phase.

Why do behavioral solutions cause friction during the holidays?

The second common approach to fighting ATO is behavioral and risk-based fraud prevention. These tools try to stop fraud by analyzing user signals – location, time of day, and device behavior.

While effective at identifying anomalies, Arad points out a significant downside: customer friction.

Consider the “Sneakers in Phuket” scenario. A legitimate customer, usually based in Israel or New York, travels to Thailand for a holiday vacation. They try to buy a pair of sneakers at 2:00 PM local time. The behavioral system flags this as an anomaly because it doesn’t match the user’s standard “10 AM to 7 PM home country” profile.

The result? The transaction is blocked, or the user is challenged with friction they can’t pass. They are forced to “leave their shoes in the store.” During the holiday season, when travel is the norm, these false positives spike. This approach focuses on the fraud aspect but fails to address the root cause – the initial phishing event that compromised the credentials.

How does ‘being there when the attack happens’ change the outcome?

To solve the limitations of “scan and takedown” (too slow) and “behavioral scoring” (too much friction), the industry must move toward real-time visibility and protection.

Memcyco’s core differentiation is the capability to “be there when the attack happens.”

Unlike scanners that look from the outside in, Memcyco embeds a digital watermark (Nano Defender) into the legitimate brand site. When an attacker copies the site to create a clone, they inevitably copy this sensor. This provides the retailer with an embedded agent inside the attack itself.

This shifts the defense from reactive to real-time:

  • Real-time visibility: You don’t just get a report that a site exists. You see the individual victims, the attacker’s device, and the credentials being compromised in the moment.
  • Real-time protection: Instead of waiting for a takedown, the system can intervene immediately. It alerts the user with a “Red Alert” overlay or swaps their real password for “decoy data” to poison the attacker’s database.

This approach covers the gap that legacy tools miss. It protects the user during the window of exposure, before the takedown is complete.

How can retailers prevent account takeovers during high-traffic sales events?

Based on the “detect, protect, disrupt” framework discussed in the interview, here is the critical checklist for securing the 2025 holiday season.

Phase 1: Pre-season hardening (The October sprint)

Goal: Reduce attack surface before traffic spikes.

  1. Audit for typosquatting: Look beyond simple misspellings. Scan for homograph attacks using Cyrillic or Unicode characters that mimic your brand visually.
  2. Define your ‘window of exposure’: Measure the actual time it takes your current vendor to take down a site (e.g., 4 hours or 4 days). This is your risk gap.
  3. Implement ‘proof of source authenticity’ (POSA): Deploy active sensors (like Nano Defender) on your login and checkout pages to ensure you can detect clones the moment they go live.
  4. Calibrate behavioral rules: Review your fraud logic to account for holiday travel behaviors (e.g., the “Phuket” scenario) to minimize false declines.
  5. Integrate security & fraud teams: As Arad notes, these silos are fusing. Ensure your CISO and head of fraud share data on phishing threats before the season starts.

Phase 2: Peak season monitoring (The war room)

Goal: Real-time detection without disrupting commerce.

  1. Monitor individual victims: Shift from monitoring “sites” to monitoring “users.” Know exactly who entered credentials on a fake site so you can freeze only their account.
  2. Deploy active countermeasures: If a user lands on a spoofed site, trigger an immediate alert on their screen. Don’t wait for the takedown.
  3. Use decoy data: Configure your defense to feed fake credentials to attackers. This disrupts their ROI and renders the harvested data useless.
  4. Feed intelligence to SIEM: Push real-time attack data (attacker IP, device ID) directly into your SIEM to harden your broader security perimeter.
  5. Watch for “sleeper” activations: Be alert for accounts validated in October that suddenly wake up to drain loyalty points during the Turkey 5.

Phase 3: Post-season forensics (The cleanup)

Goal: Attribution, remediation, and preparation.

  1. Analyze the window of exposure: Post-season, review how many users were compromised while you were waiting for takedowns to process.
  2. Attribute the attack: Use the forensic data collected (attacker device, location) to group incidents and identify specific threat actor groups.
  3. Assess false positives: Review how many legitimate transactions were blocked due to rigid behavioral rules (the “shoes in the store” metric).
  4. Correlate phishing and fraud: Connect the dots between the phishing sites detected and the fraud attempts recorded. Did the victims come from the sites you identified?
  5. Plan for real-time migration: Use the season’s data to build the case for moving from “post-mortem” cleanup to “real-time” protection for 2026.

Conclusion

As Ran Arad summarizes, the market is crowded with “scan and takedown” scanners and “behavioral” friction engines. Neither addresses the core reality of 2025. Attacks happen in real-time, often during the window where you are most exposed.

To survive the peak season, retailers must adopt a solution that travels with the attack. By achieving visibility into the attack lifecycle from its earliest phases, brands can protect their customers, reduce friction, and stop the “cleanup crew” approach to cybersecurity.

Frequently asked questions

Why is “scan and takedown” considered a “post-mortem” activity?

The “scan and takedown” approach (traditional brand protection) relies on finding a fake site after it has already gone live. There is often a critical latency period – averaging one week – between the site going up and the takedown. During this “window of exposure,” the attack is fully active. Removing the site after a week is merely a cleanup operation for damage that has already occurred, rather than proactive protection.

Why do behavioral fraud solutions cause “friction” for holiday shoppers?

Behavioral solutions analyze signals like location and time to identify anomalies, which can backfire during holiday travel. A classic example is the “Sneakers in Phuket” scenario. A legitimate customer traveling for the holidays tries to buy shoes in Thailand at 2:00 PM. Because this behavior doesn’t match their home profile (e.g., New York, 10:00 AM), the transaction is blocked. This forces the customer to “leave their shoes in the store,” resulting in lost revenue and frustration.

How are attackers using “typosquatting” to bypass detection in 2025?

Attackers have moved beyond simple misspellings (like changing ‘o’ to ‘u’). Modern attacks use sophisticated Unicode and Cyrillic characters. A Cyrillic letter ‘a’ looks identical to a Latin ‘a’ to the human eye but registers as a different code to a computer. This allows attackers to create visually perfect clones that bypass standard scanning tools, making visual inspection unreliable.

What does it mean to “be there when the attack happens”?

Unlike external scanners that look for sites from the outside, Memcyco embeds a digital watermark (Nano Defender) into the legitimate site. When an attacker copies the site code to create a clone, they inevitably copy this sensor. This gives the brand an agent inside the attack, providing real-time visibility into the attacker, the specific victim, and the credentials being stolen.

How does “real-time visibility” help security teams (SOC/SIEM)?

Legacy tools often provide vague metrics like “Site Removed.” Real-time visibility provides granular intelligence: attacker IP, device ID, and individual victim data. This data feeds directly into the organization’s SIEM or anti-fraud systems. It effectively “opens their eyes,” allowing them to harden their broader security perimeter based on confirmed attack data rather than probabilistic guesses.

Digital Impersonation Fraud Specialist