Security operations coverage for AI systems
AI adoption is moving faster than security teams can monitor. Employees use Claude, Claude Code, and agentic tools daily – introducing new data flows, new connected tools, and new behaviors that no existing security stack was designed to see. Daylight builds detection and response coverage for this layer, inside your MDR service.
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What security teams are being asked to answer – and can't
AI systems now interact with sensitive data, connect to external tools, and take actions across your environment. Security teams are accountable for that risk – but lack the visibility and detection coverage to manage it.
No visibility into AI runtime behavior
What data is being accessed? Which tools are being invoked? What instructions is the model following? Most organizations have no programmatic visibility into what AI agents actually do during a session.
New attack surface with no detection layer
Prompt injection, shadow MCPs, credential access through agents, and runaway sessions are real and growing threats. None of them trigger existing EDR, SIEM, or identity detections – they require a detection layer built for AI.
Governance ≠ security operations
Audit logs and compliance dashboards tell you AI was used. They don't tell you what happened inside a session. Security operations requires detection rules, investigation workflows, and response – not just log access.
Detection and response for AI-native threats
Daylight builds detection rules on top of Claude Enterprise's runtime telemetry – both the Compliance API for governance events and OTel for session-level behavior – and investigates findings through the same MDR workflows as your endpoints, cloud, and identity.
Prompt Injection Detection
Detect attempts to manipulate AI agents through malicious instructions embedded in files, tool outputs, web content, or MCP results – before the agent acts on them.
Injection patterns in tool output streams
Untrusted web content followed by sensitive file access
Instruction override attempts in agent context
MCP & Tool Governance
Surface MCP servers your engineers are invoking that were never centrally approved – shadow AI tooling that bypasses governance and creates supply chain risk.
First-seen MCP server invocations
MCP scope drift beyond declared purpose
Personal connectors bypassing admin install
Credential & Data Exfiltration
Detect when an AI agent reads a sensitive file – credentials, keys, source code – and then makes an outbound network call in the same session.
.aws/credentials or .env access → external egress
SSH key or API token followed by curl/MCP send
Sensitive prompt content leaving the org perimeter
Identity & Permission Behavior
Track when users accumulate permanent auto-approvals on high-risk AI tool categories, or when two identities appear to share an AI account simultaneously.
Auto-approval drift on Bash, WebFetch, MCP write
Concurrent sessions from irreconcilable locations
Agent acting on behalf of a user outside business hours
Runaway & Cost Anomalies
Identify sessions accumulating tool activity with no recent user prompt – the signature of a runaway scheduled task, a misconfigured loop, or an attacker driving the agent unattended.
High tool activity with no user prompt > 15 min
Session cost 5-10x above user baseline
Off-hours activity outside normal user patterns
Data & Context Exposure
Detect sensitive organizational data entering AI prompt context at rates above user baseline – and correlate cross-project context bleed where AI agents blur data boundaries.
DLP-sensitive terms in prompt stream above baseline
Files from separated repos in the same session
Customer data accessed through personal MCP
Investigated through your MDR workflow
AI-native threats don't go to a separate team or a separate queue. Every detection enters the same investigation cycle as your endpoint and cloud alerts – with full enterprise context applied at every step.
Rules fire on AI telemetry
Detection rules run continuously against Claude Enterprise's OTel runtime stream and Compliance API. When a rule triggers, an investigation begins automatically.
OTel session behavior
Compliance API governance
Noma Security signals
Prompt Security signals
Correlated with full context
AI signals are correlated with identity, endpoint, cloud, SaaS, and business context – so the investigation understands who did this, in what role, and whether it's genuinely risky.
User identity & role
Behavioral baseline
Session reconstruction
Malicious, suspicious, or benign
Every investigation produces a verdict with a complete evidence chain – what data was consulted, what logic was applied. Not a score. A documented decision.
Full evidence chain
Expert review for judgment calls
Business context applied
Action taken in your environment
Where warranted, Daylight executes response: revoking sessions, blocking access, quarantining accounts, or escalating to experts for active incident handling.
Session revocation
Account suspension
Incident escalation
