In-App AI Bot Detection and Enforcement for Secure User Experience

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A Novel Approach to Bot Defense: In-App Enforcement Against AI-Powered Threats

The cybersecurity landscape has witnessed a significant shift in the tactics employed by malicious actors, with AI-powered bots emerging as a major concern. These sophisticated bots can mimic legitimate user behavior, rendering traditional bot detection and enforcement methods inadequate. To address this challenge, Impart Security has introduced Programmable Bot Protection, a groundbreaking solution that converges detection and enforcement within the application.

Historical Limitations of Bot Protection

Historically, bot protection has been a fragmented process, with Web Application Firewalls (WAFs) attempting to detect bots at the edge, while dedicated bot vendors relied on browser fingerprinting for detection and WAFs for enforcement. However, this approach has proven insufficient against AI-powered bots, which can evade detection by using real browsers and maintaining legitimate-looking sessions.

Impart’s Innovative Solution

Impart’s innovative solution tackles this issue by enabling teams to observe and validate the effectiveness of bot protection in a non-invasive manner. By running Programmable Bot Protection in shadow mode, teams can assess the accuracy of the solution against their own production data, without impacting live traffic. This approach allows teams to confidently enforce bot protection, backed by evidence from their own environment.

Key Capabilities

Programmable Bot Protection operates inline, within the live request path, evaluating behavior rather than relying on headers, IP reputation, or device fingerprints. By correlating activity across sessions, identities, and time, the solution detects all 21 OWASP automated threat categories, including credential stuffing, carding, inventory denial, and account-creation abuse.

Programmable Policies and Behavioral Detection

The platform’s capabilities include runtime behavioral detection, which identifies patterns that AI-powered bots cannot fake, as well as programmable policies as code, version-controlled in Git, deployed through CI/CD, and rolled back in seconds. This allows teams to maintain full ownership of enforcement logic without vendor intervention.

According to Jonathan DiVincenzo, CEO of Impart, “We solved the trust problem by letting teams see exactly what would happen before anything is enforced. When you can prove a block is safe using your own production data, enforcement stops being scary and starts being operational.”

Karan Mehandru, Managing Director at Madrona, noted, “The bot protection market accepted that enforcement in production was too risky. Impart proved it doesn’t have to be. As AI-driven attacks accelerate, the teams that can safely enforce will separate from the teams that are still watching dashboards.”

Conclusion

Impart’s Programmable Bot Protection offers a novel approach to bot defense, enabling teams to confidently enforce protection against AI-powered threats. By converging detection and enforcement within the application, the solution provides a robust defense against sophisticated bots, without impacting performance or requiring agents.