Black Friday traffic is usually a sign of opportunity for retailers. More product views, growing cart activity, and rapidly declining inventory are often interpreted as signs of strong consumer demand. But when automated traffic enters the shopping journey at scale, these signals become harder to trust.
Black Friday 2025 highlighted the scale of this challenge, with security providers reporting extensive bot activity throughout the holiday shopping season. Meanwhile, the rapid growth of AI shopping agents is making automated traffic more difficult to classify. For retailers in 2026, the challenge goes beyond stopping fraudulent transactions. It is about understanding which activity represents genuine demand, which automation is authorized, and when suspicious behavior begins to undermine business decisions.
The Real Risk of Black Friday May Begin When Retailers Can No Longer Understand Their Own Traffic
During Black Friday, retailers typically view rising traffic as a sales opportunity. More product views suggest greater purchase interest, more items added to carts indicate stronger buying intent, and rapidly declining inventory may signal a successful promotion. However, all these indicators rely on an assumption that is easy to overlook: website visits and user actions generally reflect the behavior of real consumers.
When large volumes of automated traffic enter the shopping journey, that assumption no longer always holds.
Black Friday 2025 offers a useful starting point for examining this issue. According to Queue-it’s monitoring of 138 global retailers, its systems recorded approximately 77.5 million visits on Black Friday 2025, up around 16% from the previous year. Across the Cyber Five shopping period, approximately 28% of requests came from bots or data-center sources. Although this was lower than the 36% recorded in 2024, it still indicates that retailers must handle substantial volumes of requests that cannot simply be treated as ordinary shopper activity during their busiest sales period.
The significance of this figure is not simply that a portion of traffic comes from automated or data-center sources. It is that retailers cannot assume every interaction represents an independent consumer expressing genuine purchase intent.
Consider a product that receives an unusual increase in page views shortly before a major promotion. That activity could reflect growing consumer interest, but it could also come from automated price monitoring, unauthorized scraping, inventory tracking, or purchasing bots. If the platform cannot distinguish these activities, its traffic data becomes less reliable as a basis for understanding demand.
The same problem can extend to cart activity, account creation, and checkout requests. Automated programs may generate repeated interactions that resemble consumer behavior, making it harder to determine whether a product is genuinely gaining popularity or simply attracting automated attention.
This does not mean every abnormal visit results in a loss, nor does it prove that retailers’ demand forecasts have broadly become unreliable. The more important concern is the possibility that misleading activity could influence how businesses interpret customer behavior and respond to market conditions.
Traditional Black Friday scam coverage often focuses on consumers being deceived by fake websites, misleading discounts, and phishing emails. For e-commerce businesses, however, there is a deeper concern: when attackers can generate visits, interactions, and transaction requests at scale, the resulting risk may extend beyond individual fraudulent purchases to the way retailers understand traffic, manage inventory, and assess customer trustworthiness.
From Fake Traffic to Misguided Decisions: What Can Attackers Influence?
To understand this risk, it helps to view an e-commerce platform as an interconnected decision-making system rather than simply a website that receives orders.
Product page visits influence how merchants assess demand. Cart additions and checkout activity feed into conversion analysis. Account activity contributes to customer value assessments, while inventory changes inform replenishment, purchase limits, and promotional strategies. These signals are interconnected and collectively shape how a business understands consumer behavior.
Black Friday’s promotional environment creates additional incentives for automated activity. Limited-time discounts, scarce inventory, and promotional benefits all carry economic value. When shoppers compete for a limited quantity of discounted products, attackers also have reasons to use automated programs to repeatedly check product availability, submit purchase requests, or claim promotional benefits across multiple accounts.
Different types of automated activity can target different stages of the shopping journey, and their potential consequences vary.
| Activity | What It Does | Potential Business Impact |
| Price and inventory scraping | Repeatedly queries product prices, stock levels, and promotional information | Increases backend load and allows competitors or attackers to track changes more quickly |
| Automated purchasing | Submits cart and order requests at scale | Competes with genuine shoppers for limited inventory and disrupts normal purchasing processes |
| Credential stuffing | Tests stolen username and password combinations | Can lead to account takeover and unauthorized transactions |
| Promotional abuse | Uses multiple or related accounts to repeatedly claim incentives | Consumes promotional resources intended for eligible customers |
| Inventory hoarding | Creates orders or inventory reservations without completing purchases | Temporarily reduces product availability for other shoppers |
Consider product scraping. Attackers may continuously collect pricing, inventory, and promotional information. Scraping alone is not necessarily malicious, nor does it directly consume inventory. However, it can expose a retailer’s pricing strategies, replenishment patterns, and promotional schedules. When scraping is combined with automated purchasing, attackers may identify profitable products more quickly and act before genuine shoppers have a chance to buy them.
Automated purchasing creates a more direct threat. Bots can repeatedly submit orders, exploit limited-time promotions, or compete for products with restricted inventory. Depending on how the platform handles reservations and checkout, this activity may consume limited stock availability, increase operational pressure, or prevent genuine customers from completing purchases.
Account abuse introduces another dimension. Compromised accounts may be used to redeem promotional benefits, make unauthorized purchases, or exploit customer loyalty programs. Because these activities take place through existing accounts, they may not resemble conventional high-volume bot traffic.
Kasada’s monitoring of the 2025 holiday shopping season provides an example of how these activities can extend beyond a single promotional moment. The company observed more than one million bot checkout attempts between November 1, 2025, and Travel Tuesday. Its monitoring also showed that automated checkout activity peaked in the week before Black Friday, while account takeover activity continued to rise after the main promotional period.
This shift in timing matters. It suggests that automated attacks do not necessarily occur only when traffic is at its highest, nor do they always pursue the same objective. Attackers may collect product information before a promotion, compete for inventory during the event, and exploit compromised accounts afterward.
As a result, retailers that monitor only website traffic or payment failures on Black Friday itself may miss risks developing before and after the main sales event. A more complete analysis needs to connect activity across different stages and examine whether the same devices, accounts, or automated patterns appear in product scraping, account operations, and transaction requests.
There is, however, an important distinction to make. Monitoring data confirms that these automated activities occurred and changed throughout the shopping season. It does not, on its own, prove that they caused a particular retailer to make incorrect inventory decisions. Distorted demand signals are a business risk worth investigating, not an established loss that can be inferred directly from bot traffic percentages.
The key question is therefore not simply how many automated requests a platform receives. It is whether the business can distinguish genuine purchasing behavior from activity that only appears to represent demand, and whether its operational decisions account for that difference.
Why Black Friday Scams Are No Longer Confined to a Single Point in the Shopping Journey
Black Friday scams are often portrayed as isolated incidents: attackers send fake promotional emails, consumers click malicious links, and the fraud takes place. But the risks facing retailers are more interconnected than this simplified picture suggests.
An attack can begin long before a consumer reaches the checkout page. Automated programs may first collect product information, monitor price changes, identify promotional opportunities, or test account credentials. During a major sales event, the same or related operations may move toward account abuse, promotional exploitation, and automated purchasing.
These activities do not necessarily belong to one coordinated campaign. They may be carried out by different actors with different objectives. Nevertheless, they can affect multiple parts of the same retail operation.
Consider how a promotion can be exploited at different stages. Before the event, attackers may scrape product catalogs and monitor inventory to identify valuable items. When the promotion begins, automated programs may attempt to claim limited offers or purchase products in bulk. Afterward, compromised accounts or acquired goods may be used for further fraud or resale.
The consumer-facing side of Black Friday scams also intersects with these business risks. Fake promotional websites and phishing messages can imitate trusted retailers, collect login credentials, or redirect consumers to fraudulent payment pages. These scams may not directly involve a retailer’s own website, but they can still damage brand trust and expose customers to account or financial loss.
The challenge for retailers is that different security systems often operate independently. Email security may detect impersonation attempts, while website defenses focus on automated requests. Account protection may monitor suspicious logins, and payment systems may evaluate transaction risk. Each system can provide useful information, but isolated signals may not reveal the broader pattern.
For example, a payment system may identify a suspicious transaction without knowing that the account previously showed unusual login behavior. A bot detection system may recognize abnormal product browsing but lack visibility into whether the same device later attempts to redeem promotional benefits.
This is why Black Friday security should be considered across the entire shopping journey. Retailers need to connect relevant signals from website access, product interactions, account activity, and transactions so that risk can be assessed in context rather than through disconnected events.
When Bot Traffic Becomes an Availability Risk: How Automation Disrupts E-commerce Operations
Beyond distorting business metrics and interfering with purchasing activity, automated traffic can create another operational risk during Black Friday: reduced website performance and service availability.
During major promotions, retailers must handle sharp increases in legitimate customer activity. Product searches, account logins, inventory checks, cart updates, and checkout requests all place demands on different parts of the application infrastructure. When automated requests are added to this workload, the pressure can become particularly significant, especially when they target resource-intensive functions.
Not all requests consume the same amount of system resources. A cached product page may be relatively inexpensive to serve, while an order submission can trigger inventory checks, discount calculations, database writes, and communication with downstream services. As a result, overall traffic volume may not reveal where the actual bottleneck lies. A single high-cost endpoint can reach its processing limit even while other parts of the website remain responsive.
Several components may become bottlenecks under sustained automated activity:
- Database connections: Repeated dynamic queries can consume available database connections and delay legitimate operations.
- Authentication services: Large volumes of login attempts can increase processing pressure and affect normal account access.
- Inventory management: Competing requests can delay stock updates and reservation processing.
- Order processing: Repeated purchase submissions can overload transaction-processing services.
- Retry traffic: When responses become slower, clients may send additional requests, creating further pressure on already constrained services.
Consider a simplified example. Assume an inventory service can sustainably process 2,000 requests per second under a particular tested workload. During a Black Friday promotion, legitimate shoppers generate 1,200 requests per second, while unwanted automation contributes another 1,000. The combined load reaches 2,200 requests per second, exceeding the tested processing capacity by 10%.
Without sufficient additional capacity, request rejection, or effective traffic controls, the excess workload may accumulate in queues, increase response times, or cause requests to fail. Customers may experience delayed inventory updates, unsuccessful cart operations, or interrupted checkout processes.
This is a hypothetical example for illustration, not a performance benchmark or a customer result. Actual capacity depends on request complexity, caching, infrastructure configuration, and queue management.
The business impact can extend beyond temporary website slowdowns. During a limited-time promotion, even a short period of degraded checkout performance may prevent customers from completing purchases. It can also increase customer frustration, create additional support requests, and undermine confidence in the retailer’s digital shopping experience.
Increasing infrastructure capacity remains an important part of preparation. However, adding resources alone does not address the underlying problem if unwanted automation continues to consume them. Retailers also need controls that can distinguish legitimate shopping activity from abusive requests and limit unnecessary traffic before it reaches resource-intensive application services.
The objective is not simply to handle more requests, but to preserve the availability of critical shopping functions for genuine customers when demand and automated activity peak simultaneously.
AI Shopping Agents Are Changing How Retailers Define Normal Traffic
If traditional automated attacks have already made it difficult for retailers to distinguish genuine users from bots, the emergence of AI shopping agents makes the challenge even more complex: automated access itself is no longer a reliable indicator of malicious intent.
In the past, automated visits to product pages, bulk price comparisons, or rapid form submissions were often treated as suspicious signals. However, AI shopping agents may perform similar actions on behalf of consumers. They can search for products, compare prices, check availability, and even participate in parts of the shopping process with user authorization.
According to Adobe Analytics, traffic from AI sources to U.S. retail websites increased by 393% year over year in the first quarter of 2026. These figures do not represent traffic across the entire internet, nor do they suggest that all AI agent traffic indicates purchase intent or poses a security threat. However, they highlight a growing trend: retail websites are becoming important destinations for AI-driven activity, which is no longer limited to reading publicly available webpages.
This shift is particularly relevant during Black Friday. An AI agent may compare dozens of products within seconds, while a malicious scraping tool may exhibit similar browsing patterns. An authorized AI agent may interact with a shopping cart, whereas malicious automation may attempt to submit orders in bulk. In such cases, request speed, traffic volume, and other automation characteristics alone may no longer be sufficient to determine the intent behind an action.
Retailers therefore face a challenge that goes beyond simply deciding whether to allow bots. They need to assess whether automated activity is trustworthy, whether it has been authorized, and whether the actions it performs comply with their business rules.
A Trusted Identity Does Not Automatically Mean a Trusted Action
Imagine an e-commerce platform that allows AI agents to access product prices and inventory information. That permission does not mean an agent should be allowed to scrape the entire product catalog without limits. Nor does it authorize the agent to bypass purchase limits, repeatedly claim promotional offers, or perform high-risk account and payment operations.
Similarly, even when an automated client can be identified, retailers still need to determine whether it is operating within the scope of its authorization. Authentication answers the question of who is accessing a system. Authorization and behavioral risk analysis address two different questions: what that entity is permitted to do and whether its actions are appropriate.
For Black Friday 2026, retailers should consider establishing tiered policies for automated access. Which product information can be accessed publicly? Which requests should be rate-limited? Which operations require account verification? And which transactions should undergo additional risk assessment?
This approach also helps avoid the opposite mistake: blocking all AI-driven access indiscriminately. As AI shopping becomes another way for consumers to discover products, overly restrictive policies may affect product visibility and potential sales opportunities. Unrestricted access, on the other hand, can leave room for automated abuse.
A more sustainable approach is to assess risk dynamically based on identity, permissions, behavior, and business context, rather than deciding solely on whether a request was initiated by software.
For Black Friday 2026, Retailers Need to Rethink Their Risk Assessment Logic
If automated activity can influence product browsing, account operations, and checkout processes at the same time, security strategies cannot rely on a single isolated metric. Retailers need risk assessment mechanisms that account for behavioral context and align security responses with actual business processes.
Distinguish traffic growth from genuine demand.
Visits, cart additions, and account numbers can reflect business activity, but they do not automatically represent real consumers or purchase intent. Businesses should validate these indicators against completed orders, payment outcomes, account histories, and inventory changes instead of treating abnormal activity as direct evidence of promotional success.
For example, an unusual increase in product views should not automatically trigger a replenishment decision. Retailers should examine whether the increase is accompanied by credible cart activity, completed purchases, and consistent customer behavior. The objective is not to dismiss traffic data, but to understand what it actually represents.
Connect risk signals across different business touchpoints.
A single login, product view, or order may appear normal on its own. However, when multiple actions share device characteristics, behavioral patterns, or account associations, they may reveal a broader pattern of automated abuse.
Risk analysis should cover the wider user session and transaction journey rather than examining individual requests in isolation. This is particularly important when attackers distribute activity across multiple accounts, devices, or network sources to avoid simple detection thresholds.
Apply different responses to different levels of risk.
Normal users and trusted automated access should retain a smooth experience wherever possible. For access that cannot be verified, exceeds its authorized scope, or exhibits abnormal behavior, retailers can apply rate limits, additional verification, or restrictions on sensitive operations.
Blocking all automated traffic may disrupt legitimate business activity, while relying solely on fixed thresholds can allow attackers to evade detection by adjusting their request patterns. A risk-based approach allows retailers to respond proportionately instead of treating every unusual interaction in the same way.
Evaluate security through business outcomes.
Retailers should expand their post-Black Friday security reviews beyond the number of attacks blocked. They should also examine whether security incidents affected business outcomes. Relevant indicators include abnormal account creation, promotional abuse, inventory reservation, payment failures, false-positive rates, and checkout conversion.
Connecting security events with business performance helps retailers determine whether their defenses are actually reducing risk rather than simply increasing the number of blocked requests. It also makes it easier to identify where security controls may be creating unnecessary friction for legitimate customers.
Building a More Resilient Defense Against Black Friday Scams
Addressing Black Friday scams requires more than adding another CAPTCHA or treating every automated visit as an attack. Retailers need a defense strategy that combines traffic identification, behavioral analysis, access control, and business risk assessment.
Risk-Based Traffic Identification
The first step is to understand the nature of incoming traffic. Rather than relying on request volume or IP reputation alone, retailers should consider behavioral patterns, device environments, network characteristics, and request context. These signals can help distinguish ordinary shopping activity from automated behavior that requires further investigation.
The objective is not to eliminate all automation. It is to identify activity that is inconsistent with legitimate use, exceeds acceptable access patterns, or creates risks for business operations.
Cross-Session and Account Risk Analysis
Automated abuse does not always take place within a single session. Attackers may distribute activity across multiple accounts, rotate network sources, or reuse similar device environments to make individual requests appear unrelated.
Connecting device, account, and behavioral signals can help reveal relationships that are difficult to identify through isolated checks. This is particularly useful for detecting patterns associated with bulk registration, account takeover, promotional abuse, and automated purchasing.
Adaptive Verification and Access Control
Not every user or automated request requires the same level of verification. Retailers should apply controls according to the sensitivity of the action and the level of risk involved.
Low-risk browsing may require little or no additional friction, while suspicious registration, login, promotional redemption, or checkout activity may warrant additional verification. Requests that exceed authorized limits can be rate-limited, while clearly malicious activity may be blocked.
This layered approach helps protect critical business processes without unnecessarily disrupting genuine consumers or legitimate AI shopping agents.
Business-Aware Risk Management
Security decisions should also reflect the business context in which activity occurs. A sudden increase in product views, for example, should be assessed differently from repeated attempts to claim a limited promotional offer or submit multiple orders from related accounts.
Retailers should define risk policies around specific business operations, including inventory access, promotional eligibility, account management, and payment authorization. By connecting security signals with these rules, businesses can make more consistent decisions about when to allow, verify, restrict, or block an action.
Finally, these controls should be reviewed continuously. Black Friday traffic patterns change quickly, and attackers can adjust their behavior in response to existing defenses. Monitoring false positives, conversion rates, and operational impact is therefore just as important as tracking blocked attacks.
Conclusion: The Next Stage of Black Friday Security Is Knowing Which Activity Deserves Trust
Black Friday scams are no longer just a matter of fake promotional emails, fraudulent websites, or isolated bot attacks. Automated activity can affect product discovery, account operations, promotional campaigns, and checkout processes. Meanwhile, the rise of AI shopping agents is making the distinction between legitimate and malicious automation increasingly complex.
The available evidence does not prove that every retailer has already experienced distorted demand signals. It does show that relying solely on traffic volume, account numbers, or the characteristics of individual requests may no longer be sufficient for assessing risk in the evolving e-commerce environment.
For Black Friday 2026, retailers need to look beyond how many requests they block. They must understand which interactions come from genuine consumers, which automated visits have appropriate authorization, and when abnormal behavior begins to affect inventory, transactions, and customer experience.
The next challenge in Black Friday scam prevention is not simply identifying more bots. It is understanding which interactions deserve trust, which actions should be authorized, and when automated activity begins to undermine the integrity of commerce.
Black Friday security should not be a race to block bots faster. For retailers, the greater priority is to build mechanisms that understand behavioral context, assess business risk, and protect genuine transactions. Only then can businesses embrace new opportunities in automated shopping while reducing the impact of scams and malicious automation on customer trust and business decisions.
