22-Second Breach Windows Mean Your AI Enforcement Must Be Inline

Google Mandiant's M-Trends 2026 report, based on 500,000+ hours of frontline incident response, found that the median time between initial access and handoff to a secondary threat group collapsed from over 8 hours in 2022 to 22 seconds in 2025. Initial access brokers now execute the handoff as an automated subprocess of the initial infection script. The buyer's malware deploys before the victim's SIEM processes the first log entry.

Separately, AI-enabled attacks rose 89% year-over-year in early 2026. An AI-driven tool compromised 600+ FortiGate firewalls across 55 countries with zero human operator involvement. IBM responded by launching an Autonomous Security Service on April 15 specifically to counter machine-speed threats.

I want to focus on what this means for organizations running AI in production. Your AI deployments - copilots, agents, model API integrations - are high-value targets inside your environment. When an attacker reaches them (or when an insider abuses them), the damage happens through AI API calls: data exfiltration via prompts, unauthorized model access, sensitive data flowing to unsanctioned models. At a 22-second attack tempo, your AI enforcement architecture determines whether you catch it or read about it in a post-mortem.

Photo by Matt Foxx on Unsplash

AI deployments as high-value targets

An AI copilot or agent typically has broad access. It can query internal databases, call external APIs, process sensitive documents, and generate summaries of confidential data. That access makes it a force multiplier for an attacker who compromises the user's session or credentials.

Three scenarios where machine speed matters:

Scenario 1: Data exfiltration via prompts. An attacker with a compromised employee session uses the internal AI assistant to extract sensitive data. "Summarize all customer records from Q1". "List all employees with access to the payment system". Each prompt returns data in seconds. An automated script fires these requests at API speed - dozens of calls in milliseconds, each returning structured data. The exfiltration completes before your SOC processes the first log entry, and the traffic looks identical to normal AI usage.

Scenario 2: Unauthorized model access. An insider routes proprietary data to an external model API. Source code, financial projections, customer PII - pasted into prompts to an unsanctioned model. The data leaves your environment as an HTTPS POST. Your DLP sees encrypted web traffic. I covered this visibility gap in detail in Shadow AI to $670,000 Blind Spot.

Scenario 3: AI agent privilege abuse. An AI agent with elevated permissions (database access, API keys, tool execution) gets weaponized through prompt injection or session hijacking. The agent makes legitimate-looking API calls to the LLM, receives tool_use instructions, and executes them. Every call is authenticated, authorized at the IAM layer, and completely malicious.

In all three scenarios, the damage flows through HTTP AI traffic - requests to model APIs, responses containing sensitive data, tool invocation decisions. And in all three, the window between "first malicious request" and "data is gone" is measured in seconds.

How most organizations handle AI traffic today

Most organizations I talk to fall into one of two buckets: they log AI traffic after the fact, or they have zero visibility into it at all.

The logging approach works like this: the AI traffic hits the model API, the request and response are logged asynchronously, and a batch process picks up the logs later for classification and alerting. Response time is 15 minutes to hours, depending on the batch cycle. At a 22-second attack tempo, the attacker completes the exfiltration, clears context, and moves on before the first log reaches your SIEM. You have a forensic record (if the logging is comprehensive), but zero prevention capability.

This architecture works for compliance reporting. As a security control, it is structurally incapable of stopping anything at machine speed.

What enforcement at machine speed requires

The enforcement decision has to happen before the request reaches the model. The AI traffic passes through a synchronous evaluation layer that checks every request against policy - identity, data classification, model authorization - and makes a pass/block decision before the request leaves your environment.

A blocked request never reaches the model. A blocked response never reaches the user. The attacker's first malicious prompt gets denied in real time.

The evaluation itself is a policy lookup with data classification. It runs in single-digit milliseconds. The LLM inference that follows takes 500ms to 5 seconds. The enforcement overhead is invisible relative to the model's response time.

This is exactly what DeepInspect does. More on that below.

Regulations

Fannie Mae just mandated AI governance for every mortgage lender in the US, effective August 8. Freddie Mac's version is already live since March. The EU AI Act's high-risk system requirements take effect August 2. Texas and California have AI governance laws in force since January. Each of these frameworks requires the same thing: know which AI tools are in use, tie every interaction to an identity, enforce policies, and produce audit-ready evidence on demand.

Inline enforcement on AI traffic is the architecture that satisfies both the security requirement (machine-speed prevention) and the compliance requirement (identity-bound audit trails). This architecture is becoming table stakes for any organization running AI in a regulated environment.

DeepInspect

This is the problem DeepInspect was built to solve. DeepInspect sits inline between your users/agents and the LLM APIs they call. For every request and response, it evaluates identity, data classification, model authorization, and organizational policy - and makes a pass/block decision before the traffic reaches the model.

Every decision - allow, block, redact - is recorded with the user identity, timestamp, data classification, and the specific policy rule that applied. That audit trail turns a security incident from "something happened with AI" into "user X made 47 requests containing PII to an external model between 2:14 and 2:16 AM, all blocked".