Introduction
As enterprises race to embed generative AI into daily operations, security teams are confronting a new category of threats that traditional monitoring systems were never designed to handle. From AI-powered workflow automation to code generation and document analysis, enterprise AI platforms are rapidly becoming operational infrastructure. But with that shift comes a growing concern: organizations often lack visibility into how AI systems are being used, what data they access, and whether those interactions introduce security exposure.
That challenge is driving a new market for AI-native security monitoring. This week, Daylight announced that it is allowing organizations to detect and investigate AI-native threats tied to enterprise AI usage. The move positions Daylight among the first MDR providers focused specifically on monitoring risks emerging from enterprise AI environments rather than solely from traditional SaaS, cloud, or endpoint infrastructure.
The rise of AI-native threats
AI adoption across enterprises has accelerated dramatically over the past year. Organizations are increasingly using tools like Claude Enterprise for summarizing internal documents, generating software code, automating repetitive workflows, and connecting AI systems to broader business applications. But as AI becomes embedded into everyday work, security teams are discovering new blind spots.
According to Daylight, risks now extend beyond conventional cybersecurity concerns into areas unique to AI ecosystems. These include unauthorized or risky MCPs (Model Context Protocol integrations), malicious prompt injection attempts, unsafe plugins and Skills, suspicious file interactions, and unusual AI-driven behavior patterns. Such threats are fundamentally different from traditional malware or phishing attacks because they exploit the very capabilities that make AI valuable: natural language processing, contextual understanding, and autonomous decision-making.
For instance, prompt injection attacks can trick an AI model into revealing sensitive information or performing actions outside its intended scope. Unauthorized MCPs could allow a malicious actor to gain backdoor access to corporate knowledge bases. Similarly, an employee might inadvertently connect an AI tool to a business application with excessive permissions, creating a pathway for data exfiltration. These scenarios are not hypothetical—security researchers have already demonstrated proof-of-concept attacks against major AI platforms, and the threat landscape is evolving rapidly.
Claude Enterprise, like other enterprise AI offerings, has begun exposing more activity telemetry through audit logs and compliance-focused APIs. This gives organizations deeper visibility into how employees interact with the platform—what prompts they submit, which files are uploaded, what tools are invoked, and which external integrations are connected. However, raw telemetry alone does not necessarily help security teams determine whether a specific activity represents a real threat. A spike in file uploads could be legitimate research work, or it could be a data exfiltration attempt. The nuance is where Daylight says its MDR platform fits in.
Daylight's MDR integration: From telemetry to investigation
Daylight's managed detection and response service is designed to bridge the gap between raw AI telemetry and actionable security investigations. When a potentially risky activity is identified, Daylight correlates AI usage with broader identity, SaaS, cloud, endpoint, and operational context. The goal is to help organizations determine not only what happened, but also who initiated the activity, what systems or data were involved, and whether the event represents meaningful business risk.
This broader contextual approach reflects a growing realization in cybersecurity that AI systems cannot be monitored in isolation. AI activity increasingly intersects with sensitive business workflows, internal repositories, developer environments, and third-party integrations. For example, a user might query a Claude Enterprise model to generate code that references a private API key stored in a company's internal documentation. Without context, that activity might appear benign. But when correlated with identity and access controls, it could be flagged as an attempt to leak credentials.
Daylight's solution integrates directly with Claude Enterprise's logging capabilities, ingesting events related to conversations, file attachments, tool calls, and configuration changes. The MDR service then applies machine learning models specifically trained on AI usage patterns to detect anomalies. These models can identify unusual prompt frequencies, unexpected API calls, or deviations from typical user behavior. Once a detection is made, the service automatically opens an investigation, assigns a severity level, and provides a recommended response—such as quarantining a compromised account or revoking an unauthorized integration.
“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 service is available as a 24/7 managed offering, meaning that a human analyst reviews each alert to confirm its validity before escalating to the customer's security team. This reduces false positives and ensures that security operations centers can focus on genuine threats.
The standardization of AI security monitoring
Daylight says the current integration is only the beginning of broader AI security coverage. The company plans to expand visibility into additional AI telemetry sources, including prompts, tool calls, Skills, and agent workflows as enterprise AI platforms expose more logging capabilities and OpenTelemetry support. The company also expects similar auditability standards to emerge across competing enterprise AI ecosystems, such as OpenAI's ChatGPT Enterprise, Google's Vertex AI, and Microsoft's Azure OpenAI Service.
This evolution is reminiscent of the early days of cloud security, when organizations had to build custom monitoring for AWS, Azure, and GCP before standardized tools like Cloud Security Posture Management (CSPM) emerged. Analysts predict a similar trajectory for AI security: initially fragmented, with point solutions for each AI platform, followed by consolidation into unified platforms that can monitor multiple AI services from a single dashboard. Daylight's strategy appears to be to establish itself early as the go-to MDR provider for AI workloads, leveraging its deep integration with Claude Enterprise as a beachhead.
Industry observers expect this category of AI observability and AI detection tooling to expand rapidly as enterprises move from limited experimentation to large-scale deployment of generative AI platforms. According to a recent survey by Gartner, 60% of organizations with more than 5,000 employees plan to deploy generative AI in production within the next 12 months. Yet less than 10% have implemented dedicated security monitoring for those tools. This gap represents both a risk and an opportunity for vendors like Daylight.
Security operations centers have traditionally focused on endpoints, identities, networks, and cloud infrastructure. Increasingly, however, AI systems themselves may become another critical layer requiring continuous detection and response coverage. The integration of MDR with enterprise AI platforms is a logical next step—just as SOCs monitor user behavior with UEBA (User and Entity Behavior Analytics) tools, they will soon need to monitor AI behavior with similar mechanisms. Daylight's approach treats the AI model as a user or entity with its own behavior baseline, making it possible to detect deviations that indicate compromise or misuse.
Implications for security operations centers
For enterprises rapidly operationalizing generative AI, the shift toward AI-native security monitoring may soon become less optional and more foundational. The risks are not hypothetical: in early 2024, a major financial institution discovered that an employee had inadvertently uploaded sensitive client data to a public AI model through an unauthorized plugin. The incident went undetected for weeks because the organization had no visibility into AI tool usage. Since then, regulatory bodies such as the SEC and GDPR authorities have begun explicitly warning companies about the need to monitor AI interactions as part of their data protection obligations.
Daylight's announcement highlights a broader trend: the convergence of AI governance and cybersecurity. Historically, these domains were separate—AI governance focused on fairness, bias, and compliance, while cybersecurity focused on confidentiality, integrity, and availability. But as AI systems become more autonomous and connected, the lines blur. A malicious prompt injection can violate both security and governance policies simultaneously. Similarly, an unauthorized MCP could lead to both a security breach and a regulatory fine if personal data is exposed.
The MDR service from Daylight includes a dashboard that provides a unified view of AI-related risks across the organization, including metrics on data sensitivity, user risk scores, and integration health. Security teams can drill down into individual events and see the full context of an AI interaction, including the original prompt, the model's response, and any downstream actions taken by the AI. This level of visibility is critical for incident response, forensics, and compliance audits. It also helps organizations build a case for investing in AI security tools by demonstrating the potential attack surface.
Future outlook and next steps
Daylight's expansion into Claude Enterprise is likely just the first of many such integrations. As enterprise AI platforms mature, they are expected to expose more granular telemetry through APIs, including streaming logs of all model interactions, tool executions, and user sessions. This will enable even deeper security analysis, such as real-time detection of adversarial inputs, data leakage through model outputs, and privilege escalation via agentic workflows.
In the coming years, we may see the emergence of specialized AI security operations centers (AISOCs) that combine traditional SOC skills with machine learning engineering and natural language processing expertise. These teams will need to understand both how AI models work and how attackers can exploit them. Companies like Daylight are positioning themselves to supply the technology and managed services that these AISOCs will rely on.
For now, the message to security leaders is clear: if you are deploying generative AI in your enterprise, you need a plan for monitoring it. Raw telemetry from platforms like Claude Enterprise is a start, but without the context and analysis that MDR provides, it is like having a smoke detector without a fire department. Daylight's offering aims to fill that gap, turning AI activity data into a proactive defense against emerging threats.