Attack Profiles: Taking Bot Management Beyond Anomaly Detection

Web applications attract a barrage of automated threats: bots seeking to brute-force credentials, harvest product data, exploit checkout flows, and more. Most organizations already use bot management solutions to identify and block malicious requests, but blocking bots alone doesn’t solve a deeper challenge: understanding the specific threats behind those attacks and what they are actually doing. 

Existing reporting capabilities in bot management tools often revolve around broad metrics—volumetric spikes, generic “attack types” (e.g., scraping or ATO), or simple dashboards that track how many requests were blocked. These approaches can reveal general patterns but fail to distinguish multiple distinct attackers who may fall under the same umbrella category, and they often bury stealthy, low-volume threats that never cause a measurable spike.

A single “scrapers” label on your dashboard might include everything from low-effort scripts to advanced bots without clarifying who is behind each wave of malicious requests or how adversaries evolve. 

A more granular approach—one that focuses on individual attack profiles rather than just overall volume or generic labels—can help teams see precisely which bots are on their application, how each bot changes tactics, and which endpoints they are targeting. In the sections ahead, we’ll explore the gap left by volumetric anomaly detection and general attack-type labels, then discuss how breaking malicious traffic into distinct profiles offers deeper insight and more effective bot management.

Bryan Becker, Senior Director of Product Management at HUMAN details how HUMAN Sightline uses attack profiles to give deeper insights into attacker behavior.

Context matters: The limits of volumetric anomaly detection

When security teams have only high-level metrics (“we blocked 5 million bad requests today”), they’re stuck in a reactive state. They may not realize that the “5 million” number includes several different attack types targeting different endpoints. The points below illustrate where purely volumetric reporting can lead security teams astray

Traffic spikes don’t tell the whole story

When a dashboard flags an unusually high volume of malicious requests, it may prompt further analysis—but only for the obvious, high-volume incidents. Smaller, more selective attacks may never stand out. Reporting that hinges on volume alone offers little insight into the specific methods attackers are using or how those methods differ from one group to another.

Conflation of distinct threats

One traffic surge may actually be multiple distinct attacks happening at the same time. A brute-force login attack, a content-scraping operation, and a gift-card enumeration script could coincide, but a purely volumetric dashboard will just show “suspiciously high traffic” across several endpoints. Untangling which set of requests belongs to each attacker is time-consuming without deeper behavioral grouping.

Lack of business-critical insights

Even when volumetric reporting flags a spike in blocked requests, it rarely explains why the requests pose a specific risk to the business. If an attacker focuses on regulated pages—such as alcohol, pharmaceuticals, or financial data—volume-based reports might not show that those high-liability endpoints were being quietly targeted. Teams see blocked requests but not enough detail to raise the alarm about potential compliance or fraud concerns.

Inefficient investigations

When all malicious traffic is lumped together, root-cause analysis becomes cumbersome. Security teams pull raw data from multiple sources, filter IPs or user agents, and hope they match requests to the right attack. This can lead to incomplete findings and unchecked “low and slow” threats. In many cases, lower-volume attacks go unexamined simply because the reporting tool doesn’t isolate them as distinct entities.

Attack profiles: Going beyond the “bot-or-not” paradigm

All of these challenges point to one underlying issue: knowing a request is malicious does not tell you enough about the behavior of that threat on your application or what goals they have in mind. While the early wave of bot management solutions successfully tackled large, obvious traffic anomalies, modern adversaries have adapted. The real question is no longer “Is this a bot?” but “What bot is this? What exactly are they doing? How are they evolving over time?”

In modern security operations, it’s not enough to identify that malicious requests are present; teams also need to understand and track the distinct adversaries behind them. This is where attack profiles come into play. Rather than stopping at raw traffic volume or aggregate bot activity data, grouping traffic into attack profiles that represent groups of malicious requests thought to be from the same attacker based on their characteristics and actions. Analysts can see the distinct profiles on their application and understand which parts of the site they are targeting, their capabilities, and how their behaviors compare to legitimate humans. Monitoring individual profiles over time enables organizations to react to attackers’ specific adaptations and continue to block attackers even as they change tactics.

How it works

Attack Profiles work by applying advanced data science to malicious requests that have already been blocked. This secondary detection engine ingests past and current data, correlating attributes such as IP usage patterns, request frequencies, endpoint paths, and technical signals like browser fingerprints. By continuously comparing data over time, it identifies clusters of malicious requests that share enough characteristics to be considered the same attacker—even as attackers pivot strategies or adopt new tools.

Benefits of the Attack Profiles approach

Tracking threats over time

One key benefit of this approach is that it turns raw data into a coherent story of malicious intent. Rather than seeing a large, faceless blob of bad traffic, security teams can identify the distinct adversary behind each wave of requests. Each identified bot profile becomes a record that can be tracked over time, allowing analysts to see how that bot adapts, whether it disappears, or reemerges under new user-agent strings or IP addresses. This process frees security teams from the burden of manual log crunching and lets them focus on the strategic side of threat defense: understanding attackers’ motivations, shaping effective mitigation policies, and staying ahead of emerging tactics.

Adaptive learning insights

Another vital element of Attack Profiles is that the insights it uncovers do not end in the dashboard. These attacker-specific findings often feed back into automated detection logic, giving the system more refined parameters for real-time blocking. By identifying patterns that link seemingly unrelated malicious requests, Attack Profiles help ensure that future requests—such as a new wave of attacks from the same adversary—can be identified and flagged more quickly.

Streamlined investigations

Once malicious requests have been grouped into discrete attacker profiles, security teams can conduct investigations with precision and speed. Rather than digging through millions of blocked requests, they can zero in on a single profile and immediately see patterns in targeting, timing, or technical sophistication. This focused lens helps them address urgent threats faster and spot new or surprising behaviors that might otherwise hide in the aggregate data.

In many organizations, this level of detail can reveal hidden threats that do not generate dramatic volume spikes yet pose serious risks. A bot that only scrapes regulated products, for instance, may operate at a measured pace to evade normal detection thresholds. Without a mechanism to correlate distinct but subtle patterns, defenders might remain unaware of its activities for weeks or months. By contrast, Attack Profiles readily spot unifying trends within malicious traffic and bring them to light in a single, actionable view.

Root-cause analysis

The ability to isolate a bot’s behavior over time also aids with root-cause analysis and post-incident review. When executives ask why a particular endpoint or product category was hammered by malicious traffic last week, investigators can look at the relevant profile to see how many requests it generated, which specific IP addresses were used, and whether the attacker had attempted a similar assault in the past.

Improved stakeholder communication

In addition, these attack profiles can be used to deliver clear, data-backed reports to leadership. Instead of saying that the organization blocked several million malicious requests in a given period, security leads can point to the precise adversaries behind those requests, the nature of their activity, and how much progress has been made in thwarting them. 

Moving beyond the numbers

Anomaly detection can catch spikes, but it doesn’t show how many separate threats may be hiding in that surge or how they adapt over time. By analyzing flagged traffic to create distinct attacker profiles, security teams transition from a raw count of “blocked requests” and high-level attack data to a more detailed view of what’s happening behind the scenes. This approach reveals which campaigns are still active, how they overlap with one another, and whether a bot is changing its technique to avoid newly introduced defenses.

Such context informs decisions far beyond the SOC. Incident response teams gain a clearer picture of which threats should be prioritized, compliance officers can see whether specific regulations might be at risk, and leadership has concrete evidence of what types of attacks are ongoing. Ultimately, the conversation moves past how many malicious requests were stopped and focuses on how each profile evolves, what it’s targeting, and what adjustments can keep it contained. By putting adversary behaviors at the center of post-detection reporting, organizations can refine their overall security posture and react more decisively to emerging threats.

See Attack Profiles in action with HUMAN Sightline

HUMAN Sightline revolutionizes bot management by delivering unmatched visibility into specific attackers and their evolving tactics. Leveraging advanced AI, HUMAN Sightline automatically isolates malicious traffic into distinct attack profiles, allowing analysts to dive deep into each adversary’s behaviors—including targeted routes, IP addresses, regions, evasion techniques, and precise indicators used to identify them. This granular intelligence empowers security teams to move beyond generic bot detection, continuously adapt defenses as attackers pivot, streamline investigations with unprecedented speed, and clearly communicate threats and mitigations to stakeholders.

Learn more about HUMAN Sightline here, or reach out to sales to book a demo.