The Pressure on the Blue Team: Machine-Speed Adversaries, Alert Volume and Scarce Talent

All courses / AI Blue / Module 1 / Chapter 3

Learning objectives

After the lesson the participant can:

• Compare what agency assessments, developer and threat-intelligence reports and laboratory studies can and cannot show about adversaries' use of AI
• Classify the chapter's pressures as observable, expected or speculation and justify each label
• Explain the four reasons why speed alone does not justify autonomy
• Draft a pressure register entry with evidence, label, local measure, threshold and decision

Key concepts

Pressure register

One reviewed file that keeps the pressures bearing on the team, each with its evidence, its label, a local measure, a threshold and the decision it informs, so the team does not respond to the loudest headline or a vendor's account of urgency.

Exploitation window

The time between a vulnerability becoming known and its exploitation. Mandiant reported an average of 5 days for vulnerabilities disclosed in 2023, down from 63 days in 2018 and 2019, and M-Trends 2026 an estimated mean of minus seven days for 2025.

Four ways out of the capacity arithmetic

When demand exceeds triage capacity a team can lower the triage time per alert, which automation offers, lower the inflow, which detection engineering offers, add people, which the labour market limits, or accept that alerts are closed unread, the failure the first three exist to avoid.

Capability versus use

Laboratory studies show what AI systems can do under test conditions, which is different from what adversaries are documented to do. The chapter keeps the two apart when it weighs the evidence.

Design assumption

Machine-speed adversaries as the normal case for a European organisation is the book's design assumption, not a forecast: an inference the team plans for and tests by drill.

Interactive exercises

Do them here and get feedback at once. Choose a card and then where it belongs, or drag it.

Teaching plan

Discussion

1. ANSSI reported no confirmed AI-assisted attack against French actors in February 2026, while GTIG reported an AI-developed exploit in a criminal plan in May 2026. How can both statements be held without contradiction?
2. The chapter admits that no source in its bibliography measures telemetry growth across organisations. How should a team argue for investment when the evidence for its own load is local?
3. The chapter argues that cutting the team is not a response to pressure. What arguments would you expect from a budget holder, and what evidence would answer them?

Written exercise

Self-test

Choose an answer to see whether it is right, or open the answer below each question.

1. In the chapter's assumed illustration for a team shaped like Nordvik Region's, what is the relation between demand and capacity in the staffed hours?

   1. 600 alerts against a capacity of 300, so half of them go unread
   2. 300 alerts against a capacity of 225, so demand is 1.3 times capacity
   3. 225 alerts against a capacity of 300, so the team has spare capacity for hunting
   4. 300 alerts against a capacity of 600, so the provider is not needed
   Show the answer

   B. 300 alerts against a capacity of 225, so demand is 1.3 times capacity
   Six analysts with five productive hours and eight minutes per alert give 225 alerts a day; 300 arrive in staffed hours, so demand is 1.3 times capacity. The figures are an assumed illustration, not measurements.

2. Which label does the chapter give to fully autonomous campaigns at scale against European targets?

   1. Observable, because developers have reported such campaigns
   2. Expected, because ENISA judges them highly likely
   3. A design assumption that needs no evidence
   4. Speculation, because no source documents it
   Show the answer

   D. Speculation, because no source documents it
   The label table marks this row speculation: no source documents it, and ENISA names experimentation only as possible.

3. Which of these is one of the chapter's four reasons why pressure does not justify autonomy on grounds of speed alone?

   1. Speed that is wrong is faster to cause harm, such as a containment that wrongly isolates a production system
   2. AI systems are too slow to match adversaries whose tooling works in bursts of seconds
   3. European law prohibits any automated containment action in a SOC
   4. Adversaries have not yet been observed using AI anywhere along the attack chain
   Show the answer

   A. Speed that is wrong is faster to cause harm, such as a containment that wrongly isolates a production system
   The first reason is that a wrong containment is an incident of the team's own making. The others are also that the documented adversary still works with people, that a faster queue does not relieve the exploitation window, and that cutting the team is no response.