Why can't my existing SIEM and EDR handle AI incidents?

They can handle the parts that touch logs and endpoints. Neither reads the prompt content inside an AI API call, so the identity, data, and policy context that defines an AI-agent incident never reaches them unless a separate layer emits those events.

Do I need a new tool, or can DLP cover AI traffic?

Network DLP runs underneath TLS and classifies documents, so it cannot inspect the free-text prompt inside an encrypted API call without specific TLS inspection and payload parsing for each provider. For the prompt channel, an AI traffic enforcement layer is the fit.

What is the one tool category most stacks are missing?

The AI traffic enforcement and audit layer. Most 2026 stacks have SIEM, EDR, SOAR, and DLP already, and lack the inline layer that reads and governs the model calls themselves, which is where AI incidents actually occur.

How does the AI traffic layer work with SOAR?

It emits a structured, per-decision audit record that a SOAR playbook consumes as a trigger and evidence source. The playbook can then revoke a delegation, open a ticket, and package regulator evidence automatically, because the record already carries identity and policy context.

Does this help with EU AI Act compliance?

For high-risk systems, Article 12 requires automatic event logging that supports traceability. The incident record needs to cover the complete system workflow. A request-boundary record contributes evidence for routed model calls, while application decisions, tool authorization, and downstream actions need records from their own control owners.

AI Incident Response Tools: The Categories a 2026 Stack Actually Needs

Google Cloud's M-Trends report describes incident investigations where response time determines the available evidence. An AI incident adds a request path that endpoint and network tools can miss: an authenticated user or agent sends a prompt to a model endpoint over HTTPS. I want to map the five tool categories that contain and document that incident, including the request-boundary record the other categories consume.

TL;DR

An AI incident response stack needs five categories: SIEM for aggregation, EDR/XDR for endpoints, SOAR for workflow, DLP/CASB for data movement, and an AI traffic enforcement and audit layer for the model calls themselves. The first four were built before agentic AI and cannot see inside the prompt, which is why the fifth category exists.

SIEM and log aggregation

Security information and event management platforms (Splunk, Microsoft Sentinel, Elastic) are the aggregation backbone. They collect logs from across the estate and give responders a single pane with correlated events for search and alerting. For AI incidents, a SIEM is only as useful as the AI-specific events fed into it. If the AI request layer emits nothing structured, the SIEM shows the network connection to the model provider and misses the prompt plus its authorization context. The category is necessary and depends entirely on the quality of the AI-traffic events upstream of it.

EDR and XDR

Endpoint detection and response, and its extended cousin XDR, watch process execution and file activity on hosts, along with lateral movement. When an AI-assisted attack touches an endpoint (a downloaded payload, a spawned shell), EDR is the right tool. Its boundary is the endpoint. An agent-to-model API call that exfiltrates data through a legitimate HTTPS request to a provider looks like ordinary web traffic to an endpoint agent, because the sensitive content is in the request body, not in any host-level artifact EDR inspects.

SOAR

Security orchestration and automated response tools run the playbook after an alert: create the ticket, enrich the indicators, revoke access, notify the team. SOAR is where investigation and recovery get automated. It acts on what it is fed, so a SOAR playbook for an AI incident needs a structured record of which identity authorized which model call under which policy. Given that record, a playbook can revoke a delegation and package regulator evidence automatically. Without it, the playbook stalls at the step where a human has to reconstruct what the agent actually did.

DLP and CASB

Data loss prevention and cloud access security brokers watch for sensitive data leaving the organization. They classify documents and match patterns on outbound traffic. The structural limit for AI is that network DLP runs underneath TLS and cannot inspect prompt content unless TLS inspection is configured for AI provider domains and the API payload is parsed field by field. DLP also classifies documents rather than the free-text context window of a prompt, so a paragraph of PHI typed into a chat box slips a document-oriented classifier. The category matters for file exfiltration and leaves the prompt channel open.

The AI traffic enforcement and audit layer

The fifth category exists because the first four cannot read the AI request. This layer sits inline between authenticated users and agents and the LLM endpoints they call. It evaluates each request against identity, role, data classification, and policy, blocks the ones that violate it before they execute, and writes a per-decision audit record of what happened. For incident response, it supplies two things nothing else in the stack can: containment at machine speed and an identity-bound record of what each model call asked and returned. This is the category DeepInspect occupies.

How to assemble the stack

Start with the incident workflow described in NIST SP 800-61r3: govern, identify, protect, detect, respond, recover, and improve. The AI incident response playbook places the same sequence around an agent or model incident. The AI traffic layer supplies the request evidence the SIEM aggregates and the SOAR playbook uses. The other four categories retain their existing jobs, including endpoint remediation and data controls.

DeepInspect

This is the gap DeepInspect closes. DeepInspect is a stateless proxy that sits inline between your users and agents and the LLM APIs they call. For every request it evaluates who is asking, what role they hold, what data is involved, and organizational policy, then makes a pass or block decision before the traffic reaches the model, with enforcement overhead under 50 milliseconds in internal testing. That is machine-speed containment at the one point in the stack where an AI incident can be stopped rather than recorded.

For every decision it commits an identity-bound, tamper-evident audit record before the response returns, written independently of the application. That record is the AI-specific event your SIEM was missing and the structured trigger your SOAR playbook needs. DeepInspect works alongside existing SIEM, EDR, SOAR, and DLP deployments. AI audit log chain of custody explains the evidence path an investigator needs after containment. Book a demo today.