Daylight is extending MDR into Claude Enterprise to tackle new AI security threats.

      Daylight is expanding its managed detection and response services to include Claude Enterprise, transforming AI activity data into actionable security investigations. This shift indicates a growing recognition of enterprise AI platforms as critical infrastructure that needs ongoing monitoring.

      As businesses rush to integrate generative AI into their operations, security teams are facing a new type of threats that were not anticipated by traditional monitoring systems. With the rise of AI-powered workflow automation, code generation, and document analysis, enterprise AI platforms are quickly becoming essential operational infrastructure. However, this transition raises significant concerns, as many organizations struggle to track how AI systems are utilized, what data they access, and whether their interactions pose security risks.

      This challenge is spurring the development of a new market for AI-focused security monitoring. This week, Daylight announced its capability to help organizations identify and investigate threats related to AI usage in enterprise settings.

      Daylight is distinguishing itself as one of the first managed detection and response (MDR) providers aimed specifically at monitoring risks arising from enterprise AI environments rather than just from conventional SaaS, cloud, or endpoint infrastructure.

      The emergence of AI-related threats is notable, as the pace of AI adoption in enterprises has surged dramatically over the past year. Organizations are increasingly utilizing tools such as Claude Enterprise for tasks like summarizing internal documents, generating code, automating repetitive processes, and linking AI systems with broader business applications. However, as these AI tools become integral to daily work, security teams are uncovering new blind spots.

      Daylight suggests that risks have expanded beyond traditional cybersecurity issues to encompass unique challenges within AI ecosystems. These include unauthorized or risky Model Context Protocol integrations, malicious prompt injections, unsafe plugins and Skills, suspicious file interactions, and atypical AI behavior.

      Claude Enterprise has started providing more activity telemetry through audit logs and compliance APIs, offering organizations better visibility into employee interactions with the platform. However, merely having telemetry data does not necessarily enable security teams to assess whether a specific activity constitutes a legitimate threat.

      This is where Daylight claims its MDR platform can make a difference. According to Hagai Shapira, co-founder and CEO of Daylight, “AI adoption is progressing faster than traditional security monitoring was designed to support. Claude Enterprise provides organizations with significant visibility, and Daylight’s MDR service converts that visibility into effective detection and response actions.”

      When a potentially dangerous activity is detected, Daylight correlates AI usage with a broader context involving identity, SaaS, cloud, endpoint, and operational data. The objective is to assist organizations in understanding not only what occurred but also who initiated the activity, which systems or data were involved, and if the event poses a substantial business risk.

      This comprehensive contextual method aligns with an increasing recognition in cybersecurity that AI activities cannot be monitored in isolation. AI usage is becoming intertwined with sensitive business workflows, internal repositories, development environments, and third-party integrations.

      Experts anticipate that this niche of AI observability and detection tools will grow quickly as organizations transition from limited trials to extensive implementations of generative AI platforms.

      Daylight asserts that the current integration is just the beginning of a broader AI security strategy. The company plans to enhance visibility into more sources of AI telemetry, such as prompts, tool calls, Skills, and agent workflows as enterprise AI platforms begin to reveal additional logging functionality and support for OpenTelemetry.

      The company also foresees the emergence of similar auditing standards across competitive enterprise AI ecosystems. This evolution has the potential to transform how security operations centers oversee enterprise environments in the coming years. Traditionally, security monitoring has focused on endpoints, identities, networks, and cloud infrastructure. However, as AI systems gain prominence, they may soon require dedicated continuous detection and response measures.

      For enterprises swiftly operationalizing generative AI, this change could soon transition from being optional to foundational.