BotShield Edge
AI-powered Bot protection for critical digital services
What is BotShield Edgeanchor
Axistwelve BotShield Edge is an on-premise Bot management platform providing real-time behavioural detection and enforcement at the application layer.
It enables organisations to detect and stop sophisticated Bot attacks that bypass traditional DDoS protection.
The platform combines high-performance traffic inspection, behaviour-driven threat detection, and AI-enabled distributed threat intelligence to deliver adaptive, real-time protection against automated threats.
Why you need itanchor
• Reduce fraud and account takeover risk
• Protect APIs and citizen services from abuse
• Maintain availability without impacting user experience
• Meet data sovereignty and compliance requirements
• Stop attacks before they reach applications
• Minimal impact on legitimate users
• Fully on-premise (data never leaves your environment)
• Scalable and highly available
The difference between Bot attacks and DDoS attacksanchor
DDoS attacks overwhelm infrastructure with traffic, whereas Bot attacks exploit application logic using low-and-slow, human-like behaviour.
DDoS protection services do not analyse behaviour, meaning Bot attacks often bypass them.
Why DDoS protection is not enoughanchor
DDoS services are designed to absorb high-volume traffic floods and protect availability. However, they do not analyse behaviour, detect credential stuffing or scraping, or distinguish between real users and Bots. Malicious Bots can therefore bypass DDoS protection and directly target applications.
Why Bot attacks are increasinganchor
Bot attacks are increasing due to the rapid growth of automation tooling and the availability of “Bot-as-a-service” platforms, which have lowered the barrier to entry. Attackers can now launch large-scale, persistent attacks with minimal effort, significantly increasing overall volume.
At the same time, the expansion of APIs and digital services has created a broader and often less protected attack surface, particularly for government platforms that rely on interconnected systems and public-facing endpoints. Credential reuse is another key driver, with Bots leveraging vast datasets of stolen usernames and passwords to carry out credential stuffing and account takeovers at scale. This poses significant risks for government services, including fraud, identity theft, and unauthorised access to sensitive citizen data.
In parallel, Bot techniques have become more sophisticated, using residential IPs, behavioural mimicry, and device spoofing to evade detection. Attacks are also increasingly data-driven, focused on extracting value through scraping, system probing, and exploitation of service logic. For government websites - where accessibility must be balanced with security - this combination of scale, sophistication, and intent makes Bot attacks an escalating and critical threat.
How BotShield Edge worksanchor
Axistwelve BotShield Edge is an on-premise Bot management solution for enterprise data centres that protects web and API services from automated threats in real time.
The service is designed to detect and stop common Bot-driven attacks such as credential stuffing, web scraping, API abuse, and L7 DDoS, while minimising disruption to legitimate users through selective challenges. It also continuously updates IP reputation and threat intelligence, improving detection over time.
Crucially for government and enterprise environments, Axistwelve BotShield Edge is fully deployed within our own infrastructure, ensuring that all traffic inspection and decision-making remain internal, supporting high availability, scalability, and strict security or data sovereignty requirements.
The BotShield Edge architectureanchor
The Axistwelve BotShield Edge platform consists of:
Traffic acquisition layer
Log processing and parsing engine
Behavioural detection engine
Decision engine
Enforcement layer
Distributed threat intelligence layer
The service works by analysing incoming traffic at the edge in real time using a behaviour-driven detection and response flow. Requests are first captured and normalised, then evaluated against defined behavioural scenarios to identify suspicious patterns. Based on this analysis, a decision engine determines whether to allow, challenge (e.g. CAPTCHA), or block the request, with enforcement happening immediately before traffic reaches backend systems. A continuous feedback loop updates threat intelligence and improves detection over time.
Built as a containerised, scalable solution, it combines Layer 7 traffic inspection with adaptive enforcement and distributed intelligence. Its approach prioritises behavioural analytics over static signatures, enabling more effective detection of evolving threats while reducing the attack surface. By separating detection from enforcement and operating within a decentralised, privacy-preserving model, the service delivers resilient, high-performance protection aligned with zero-trust principles.
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