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Security teams are confronting a new class of blind spots that traditional monitoring tools were never designed to capture.
Daylight has expanded its MDR service to integrate directly with Claude Enterprise via its Compliance API.
KEY POINTS
This integration enables detection of AI-native threats like unauthorized MCPs, suspicious Skills, and unusual prompt patterns.
Claude Enterprise now provides richer audit logs covering Claude Chat, Co-Work, and Code usage for organizational oversight.
Daylight's MDR aggregates AI activity with identity systems and SaaS data to provide contextual security investigations.
Standards like OpenTelemetry are expected to expand AI platform observability beyond audit logs to include prompts and agent workflows.
New York, USA, May 27th, 2026, FinanceWire
As enterprises accelerate the adoption of generative AI across core workflows, security teams are confronting a new class of blind spots that traditional monitoring tools were never designed to capture. From AI-assisted coding to automated data analysis and workflow orchestration, platforms like Claude Enterprise are rapidly becoming embedded in day-to-day business operations, expanding productivity while simultaneously expanding the attack surface.
Against this backdrop, Daylight is extending its Managed Detection and Response (MDR) service into Claude Enterprise, aiming to give security teams the visibility and response capabilities needed to manage AI-native threats in real time. The integration reflects a broader shift in cybersecurity: from monitoring static infrastructure to understanding dynamic, AI-driven behavior inside enterprise systems.
The rise of AI-native security blind spots
Enterprise AI adoption has reached a point where employees are no longer just interacting with models; they are embedding them into workflows that touch sensitive data, internal systems, and external integrations. Claude Enterprise, in particular, is being used for document analysis, software development, automation, and data processing at scale.
While this unlocks operational efficiency, it also introduces new security challenges. Security teams must now account for risks such as unauthorized model extensions, unapproved tool integrations, prompt injection attempts, and AI-driven access to sensitive files or systems. In many cases, these activities occur in environments that legacy security tools do not fully interpret.
Claude Enterprise has begun addressing this gap by exposing richer audit logs, offering visibility into how Claude Chat, Claude Co-Work, and Claude Code are used across organizations. However, visibility alone does not equate to security outcomes. Logs must still be translated into detections, investigations, and actionable responses.
Turning AI telemetry into a security response
This is the gap Daylight is targeting with its expanded MDR offering. By integrating directly with Claude Enterprise via its Compliance API, Daylight builds detection logic on top of audit logs to identify emerging AI-native threats.
These include the introduction of risky or unauthorized MCPs, suspicious Skills or plugins, unusual prompt patterns, anomalous file access behavior, and other signals of AI-driven activity that may indicate compromise or misuse.
Once detected, incidents are routed into Daylight’s MDR workflow, where activity is correlated across identity systems, SaaS applications, endpoints, cloud environments, and business context. This allows analysts to reconstruct not only what happened, but who initiated the activity, what systems were affected, and whether the behavior represents legitimate use or potential risk.
The company positions this as a necessary evolution of MDR, as security operations increasingly shift from endpoint and network telemetry toward AI-native observability.
“Visibility must translate into action.”
“AI adoption is moving faster than traditional security monitoring was designed to support,” said Hagai Shapira, co-founder and CEO of Daylight. “Claude Enterprise gives organizations important visibility. Daylight’s MDR service turns that visibility into detection and response.”
The framing underscores a growing industry consensus: that observability into AI systems is only the first step, and without structured response mechanisms, enterprises remain exposed to new and evolving threats.
Early enterprise adoption: Miro’s approach to AI security
One of the early adopters of the integration is Miro, which has been expanding its use of Claude Enterprise across its organization. As AI capabilities were rolled out, the company’s security team faced the challenge of ensuring that increased usage did not create new blind spots.
Rather than restricting adoption, Miro opted to integrate AI activity directly into its security operations workflow.
“As we adopted Claude Enterprise, we wanted to make sure AI usage didn’t become a new blind spot for our security team,” said Mark Strande, CISO of Miro. “Daylight helped us bring Claude activity into our MDR workflow, giving us visibility into AI-native risks and the context to investigate them.”
In practice, this has included monitoring newly introduced MCPs and evaluating whether they introduce risk based on how they interact with enterprise systems and data.
Building toward a new security standard for AI systems
Daylight’s integration is already available to enterprise customers using Claude Enterprise, and the company expects AI platform telemetry to expand further over time. As standards such as OpenTelemetry gain traction in AI environments, the scope of observable activity is likely to grow beyond audit logs to include prompts, tool calls, Skills, and agent workflows.
The company also anticipates that similar levels of auditability will become standard across leading AI platforms, including ChatGPT and Gemini, as enterprises demand consistent security controls across their AI stack.
For security teams, this signals a broader transition: AI systems are no longer experimental tools at the edge of the organization, but core infrastructure requiring continuous monitoring, investigation, and response.
As AI continues to reshape enterprise operations, the challenge is no longer whether organizations will adopt it, but whether their security models can evolve fast enough to keep up.
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