Criminal IP Launches AITEM to Advance Attack Surface Management with AI
Criminal IP introduces AITEM, an AI-powered threat exposure management platform designed to evolve traditional attack surface management by integrating diverse data sources and automating response.
Criminal IP, a cyber threat intelligence platform, is set to revolutionize attack surface management (ASM) with the introduction of its new AI-powered platform, AITEM (AI-Powered Threat Exposure Management). This initiative marks a significant shift from traditional ASM, which primarily focuses on asset discovery, towards a more comprehensive approach that emphasizes understanding, prioritizing, and responding to cyber exposures.
Traditional ASM tools have been instrumental in identifying internet-facing assets such as servers, domains, and IP addresses. However, the evolving threat landscape, characterized by increasingly sophisticated and rapid attacks, necessitates a move beyond mere discovery. "Building a safer cyber world requires a shift from visibility to action," stated Byungtak Kang, CEO of AI SPERA. "Organizations today have more visibility than ever, but many still struggle to effectively prioritize and act on the risks it reveals. By applying AI to filter noise, enrich context, and guide investigations, security teams can focus on the exposures that matter most and respond in real time, turning insight into meaningful action."
The gap between detecting a threat and effectively responding to it has widened, exacerbated by the lowering barrier to entry for attackers through AI-powered tools, readily available exploit code, and automated scanning. This means defenders must not only identify vulnerabilities but also react with unprecedented speed. AITEM aims to address this critical challenge by integrating various data streams to provide a holistic view of an organization's threat exposure.
AITEM expands the scope of ASM by incorporating external assets, open-source intelligence (OSINT), dark web data, internal infrastructure, Shadow AI, leaked data, and emerging vulnerabilities into a unified threat exposure management framework. Unlike traditional tools that often stop at discovery and provide generic risk scores, AITEM is engineered to connect disparate findings with the contextual information security teams require for thorough investigation and effective prioritization.
The platform leverages AI across four key stages of exposure management: Detection, Investigation, Prioritization, and Automation. In the detection phase, AITEM connects emerging threats and vulnerabilities to an organization's specific assets. During investigation, it allows security teams to query assets and exposures using natural language, consolidating relevant context. Prioritization involves evaluating exposures based on organization-defined risk criteria, exploitability, and real-world attacker activity, moving beyond simple vendor risk scores. Finally, automation transforms prioritized findings into actionable alerts, tickets, or workflow actions, streamlining the response process.
At its core, AITEM is built upon Criminal IP's extensive threat intelligence, providing real-world context that goes beyond basic vulnerability data. By aggregating information on open ports, exposed services, vulnerabilities, connected infrastructure, abuse history, scanner activity, and threat attribution, AITEM helps security teams understand not only what is exposed but also the surrounding threat activity and its significance.
Criminal IP's approach, as embodied by AITEM, reflects a broader industry trend towards integrated, AI-driven security operations. The company plans to showcase its vision at GovWare 2026 in Singapore, where CEO Byungtak Kang will present "From Visibility to Threat Hunting: A Case Study of AI-Driven Attack Surface Management." This session will delve into how threat intelligence and attack surface visibility can accelerate investigations and enhance security operations, emphasizing the critical shift from merely discovering exposure to actively managing and mitigating it.
The evolution of ASM is increasingly focused on operational speed and response effectiveness, rather than just asset enumeration. As Kang noted, "The competition in ASM is no longer about who finds the most assets. It will be about who can operate faster, respond more effectively, and mobilize the organization. AI should handle the repetitive analytical work. Humans should focus on judgment, accountability, and prioritization."