Bots have become a significant challenge in the digital landscape, with automated programs often being used to exploit systems and scale abuse. These malicious bots cause harm through activities such as credential stuffing, fake account creation, carding, scraping, scalping, gift card cracking, and automated booking of airline or event seats. Understanding these threats is crucial to building robust defenses.
Why protect against bot threats?
Protecting against bot threats is essential to maintaining security and operational efficiency in digital systems management. Automated programs can exploit vulnerabilities, disrupt workflows, and cause significant challenges that demand immediate and strategic countermeasures.
Bot attacks' growing sophistication and prevalence have become a top concern for organizations. Here are some key reasons to prioritize bot protection:
Sophisticated Attack Methods: Bots can mimic human behavior, bypass traditional defenses, and persist through advanced methods such as bot-as-a-service offerings.
Business Costs: Bot attacks lead to revenue loss, reputational damage, and operational inefficiencies. To mitigate these risks effectively, real-time detection and action are necessary.
How bots impact your systems
Bot attacks create significant challenges for system administrators and security teams. For example, bots generating fake accounts for card testing often bypass defenses like client-side JavaScript protections or cookie validation. Similarly, credential-stuffing attacks lead to account takeovers and financial losses, overwhelming teams with resource-intensive mitigation efforts. Attackers frequently exploit client-side misconfigurations, pinpointing and addressing the root cause is difficult.
These examples illustrate how bot-driven abuse can harm you and highlight the importance of robust protection mechanisms to safeguard against revenue loss, operational strain, and reputational damage.
Essential features and real-world benefits
Traceable offers practical solutions tailored for security teams and administrators to address the limitations of traditional bot protection tools. By combining deep analytics, robust visibility, and real-time capabilities, Traceable empowers you to stay ahead of evolving bot threats.
Bot protection against a wide range of attacks
Bot protection against a wide range of attacks focuses on identifying and mitigating automated threats such as credential stuffing, carding, and scalping that exploit APIs and web applications. By leveraging advanced detection techniques like behavioral analysis, anomaly detection, and custom security policies, businesses can prevent fraudulent activities, protect user accounts, and maintain platform integrity.
Features:
Bot Detection – Advanced automated bot detection mechanisms include volumetric detection, API access anomaly detection, browser and device anomalies, and mouse replays.
Custom Policies — Create additional security rules and actions tailored to your attack profile.
Workflow Builder — Chain multiple policies together as needed.
Action Mechanisms — This includes block, allow, header injection, visual CAPTCHA, and rate limiting.
Tracking account journey
Tracking Account Journey refers to monitoring an account’s lifecycle from creation to interactions to detect fraudulent, malicious, or compromised activity. It helps identify whether an account is legitimate, bot-driven, or taken over by attackers based on behavioral patterns and risk indicators. Traceable helps identify business-specific risk indicators to monitor account activities and take necessary actions.
Features:
Custom Entity Attribution — Define business-relevant entities through API schemas for precise tracking and security analysis. Entities may include account email, Order ID, Order Amount, Total Money Transferred, etc.
Exploratory Analysis — Use Traceable’s data lake for deep insights into entity-based risk patterns.
Workflow Builder — Chain multiple entity detection policies for enhanced security automation.
Quantifying business impact
Quantifying Business Impact involves measuring bot attacks' financial and operational effects by tracking key performance indicators (KPIs) related to fraud, revenue loss, and user experience. By analyzing attack patterns and their impact on transactions, account security, and infrastructure costs, businesses can make data-driven decisions to mitigate risks and strengthen their security posture.
Features:
Business Impact Configuration — Define and track KPIs through an API-driven schema to quantify the impact of security threats effectively.
Instrumentation
Traceable Bot Protection uses client-side JavaScript alongside API request and response analysis to detect and mitigate bot activity effectively.
Recommended Instrumentation — Edge and Inline
Traceable recommends edge instrumentation (behind CDN) and inline instrumentation for the following reasons:
Inline deployment allows real-time action on malicious traffic.
Traceable JavaScript collects browser telemetry, detecting issues like impersonation and spoofing. Deploying it at the edge ensures seamless JavaScript injection without requiring significant code changes.
Cookies for tracking detection lifecycles, maintaining states, and deriving metrics. Edge deployment ensures efficient first-party cookie management without additional customer-side instrumentation.
Supported CDNs/Edge Integrations
Following is a list of supported CDNs/Edge Integrations:
AWS Cloudfront via Lambda: Node.js, Traceable Lambda runtime extension, Python lambda
Instrumentation Method
Traceable supports multiple instrumentation methods tailored to different deployment architectures to ensure effective bot detection and mitigation. The following options provide flexibility in capturing and analyzing traffic while maintaining seamless integration with your existing infrastructure:
Behind CDN via Edge Workers.
Ensure instrumentation on all CDNs covering all North-South traffic relevant for detection and needing protection.
Tag Manager for Traceable JavaScript when instrumented on a gateway that supports JavaScript (for example, CDN, Load Balancer).
Note
If your CDN does not support response capture, another point of ingress instrumentation is required. Refer to the Data Collection section to identify alternate ingress points that can be used for data collection in such scenarios.
What is Response Capture, and Why Does It Matter?
Response capture refers to the ability to observe and collect the HTTP responses sent back from your application to clients. This capability is important for bot protection because many detection techniques rely not only on analyzing incoming requests (such as headers, IP addresses, and payloads) but also on evaluating the server’s responses — including response codes, content length, and redirect behavior.
Without response visibility, it becomes difficult to fully correlate request and response behaviors, which may lead to incomplete detection or reduced accuracy in identifying sophisticated bot activity.
Some CDNs, due to architectural or policy limitations, do not support full response capture. In such cases, bot detection systems may have limited visibility, making it harder to distinguish between legitimate and automated traffic.
Why Ingress Instrumentation Helps with Response Capture?
When a CDN is used, it often terminates the client connection and forwards only the request to the origin, with limited or no visibility into the actual HTTP response. However, bot detection often relies on analyzing both the request and the corresponding response — such as HTTP status codes, redirect behavior, or content length.
Instrumenting other ingress points such as API gateways, load balancers, or using eBPF or traffic mirroring helps restore this full visibility. These locations operate closer to the origin where both incoming requests and outgoing responses are accessible.
For example:
Gateways and Load Balancers (for example, NGINX, Apigee, AWS API Gateway) can observe full transaction flows.
eBPF provides low-level visibility into all network traffic, including responses.
Traffic mirroring captures both request and response data passively for analysis.