Adopt AI withoutadding another security problem
Daylight extends your MDR coverage to AI systems, detecting and investigating AI-native threats like agents, MCP servers, and LLM activity. No separate product, just more of your environment covered.
AI doesn’t just have access, it can act
Security tools weren’t built to follow what happens next.
AI can read sensitive data, connect to systems, and take action across your environment. But the tools built to govern AI don’t investigate threats, and the tools built to investigate threats weren’t built for AI-native activity.
AI gets the full MDR treatment
Daylight detects, investigates, and responds to AI system threats from runtime activity.
Prompt Injection Detection
Detect malicious instructions and manipulation before agents act on them.
Shadow AI and MCP Tool Activity
Detect unapproved tools, servers, connections, and changes in how they’re used.
Credential & Data Exfiltration
Detect credentials and sensitive data moving where they shouldn’t.
Identity & Permission Behavior
Detect risky access, permission changes, and abnormal identity activity.
Runaway & Cost Anomalies
Detect autonomous activity and spend that falls outside expected behavior.
Data & Context Exposure
Detect sensitive data crossing intended boundaries.
Different threat, same standard
AI-native threats get the same end-to-end investigation and response as everything else in your environment.
Continuous detection across AI activity
Every detection rule from your SIEM, cloud, identity, and endpoint tools are brought into a single library to run continuously across your stack. Compare coverage across sources, get tuning recommendations, and know exactly where you're covered.
Every signal gets enterprise context
AI activity is correlated across your security stack and business context to determine what happened and whether it represents a genuine threat.
A documented decision
Every investigation ends with a clear verdict—malicious, suspicious, or benign—backed by evidence and expert review when needed.
Action taken
When action is warranted, Daylight responds accordingly—revoking sessions, suspending accounts, escalating to experts during active incidents, and more.
AI is already in your environment, let's cover it
Frequently asked questions
AI-native security threats are risks that emerge from how artificial intelligence systems, agents, and large language models operate. Examples include prompt injection, malicious tool use, sensitive data exposure, unauthorized agent actions, and permission abuse. These threats can exploit instructions, context, and integrations rather than traditional software vulnerabilities, requiring additional visibility and security controls.
Prompt injection is an attack that manipulates an AI system's behavior through malicious instructions embedded in prompts, documents, websites, or other inputs. Detection involves examining instructions, tool calls, data access, and resulting behavior for signs of manipulation. Effective detection combines content analysis with behavioral monitoring and context about what the AI system is authorized to do.
Shadow AI is the use of artificial intelligence tools, applications, or agents without an organization's approval or visibility. Employees may use unauthorized AI services to process company information, creating risks involving sensitive data, compliance, and access control. Managing shadow AI requires discovering AI usage, understanding data exposure, and enforcing appropriate security policies.
Large language model (LLM) security protects AI systems from threats involving malicious prompts, sensitive data exposure, unauthorized actions, and misuse of connected tools. It includes securing model interactions, permissions, integrations, and outputs. Effective LLM security extends beyond filtering prompts to monitoring how AI systems behave and what actions they take within an organization's environment.
Securing Model Context Protocol (MCP) servers and AI agents requires controlling what systems, data, and actions they can access. Organizations should enforce least-privilege permissions, authenticate connections, monitor tool execution, and investigate unusual behavior. Security teams also need visibility into agent activity to detect unauthorized access, data exposure, and malicious instructions. Additionally, organizations should vet MCP servers, maintaining an allowlist of known benign MCP servers and blocking the use of any non-vetted MCP servers.
Existing EDR, SIEM, and MDR tools can detect some AI-related threats, particularly when malicious activity affects endpoints, identities, or cloud infrastructure. However, traditional telemetry may not capture prompt interactions, agent decisions, or tool execution. Detecting AI-native threats requires additional visibility into AI systems and the context needed to investigate it.
