Independence: Verifying the Loop When AI Is on Both Sides

This chapter states the design rule on which the rest of the book depends. A loop in which an emulation system produces the test and a detection system is tested by it, and in which a third system reports whether the result was good, can agree with itself. The chapter concerns P3 Observe, where telemetry, detection and response are compared with the expectation and a finding is raised; P4 Improve, where a finding becomes a change in a detection, control, playbook or mandate; and P5 Verify, where a re-test passes and evidence is attached before a finding closes. It touches P1 Target, where the scenario that is held out is chosen and by whom (Chapter 5), and P2 Emulate, whose scenarios the detection side must not see in advance. Its subject is what makes the result of such a loop independent of the thing it checks, how that independence is shown to a person who was not there, and what a loop closed by machines alone can and cannot establish.

The rule, called the independence principle in Chapter 4, reads as follows. A check counts as verification only when the organisation can show that the ways in which the check can fail are not the ways in which the checked thing can fail. Independence is therefore a property of a particular claim, designed and evidenced, and never a property that a tool, a vendor or a model family can be assumed to have. Chapter 4 declares independence in three layers: five kinds of separation (model, data, authorship, vendor and time), one rule between roles (the incentive rule) and three proofs (held-out scenarios, seeded gaps and seeded errors, and reproduction by a person). The chapter develops the five kinds and the three proofs, adds a limit that comes from test-aware behaviour, a debate on whether machines can close the loop alone, and a checklist for supervisors and boards.

Figure 15.1: Tester and tested sides sharing dependencies, the controls that break them and reproduction by a person.

Shared dependency	Control that breaks it
Model family	A critic from a second model family
Data	Held-out scenarios withheld from the drafter
Authorship	Held-out scenarios withheld from the drafter
Vendor	Disclosure of vendor and model
Time	Delayed re-test with expiry
Detection quality of the SOC agents	Seeded gaps, which measure it
Any claim made by the tester or the tested side	Reproduction by a person

Correlated failure

Consider the loop as the earlier volumes left it. An emulation planner drafts a scenario for a technique on the priority list. An emulation runner executes it inside the signed scope. An observation correlator compares the telemetry with the expectation. A detection drafter proposes a rule that closes the gap, and a critique agent reviews the rule. Each step is useful. If the same model family, the same vendor, the same retrieval corpus and the same prompts sit behind all of them, the steps are less independent than the diagram suggests, because whatever the family does not know or systematically mistakes, it mistakes everywhere in the chain.

What the research reports

Three bodies of work bear on this; what each measured comes before any interpretation. Kim and colleagues evaluated more than 350 language models on two leaderboards and on a resume-screening task and measured how often models make the same mistakes. On one leaderboard, models agreed with each other about 60 per cent of the time when both were wrong. Models that shared an architecture or a provider had more correlated errors, and larger, more accurate models had highly correlated errors even when architectures and providers differed (104, Kim et al., 2025). Panickssery, Bowman and Feng studied models used as evaluators and found that they score their own outputs higher than other texts, that they can recognise their own text with non-trivial accuracy, and that, after fine-tuning, the strength of the preference tracked the ability to recognise (106, Panickssery et al., 2024). Needham and colleagues built a benchmark of 1,000 transcripts from 61 datasets and measured whether models can tell evaluation from deployment; the best model reached an area under the curve of 0.83 against a human baseline of 0.92, with awareness higher in agentic settings than in chat (105, Needham et al., 2025).

None of these studies measured security emulation or detection engineering. The inference to the loop is the book's own: if models from one provider err together, a scenario and a detection from one family can share an omission; if an evaluator favours its own family's text, a critic on the drafter's family is a weak check on the drafter's rule; and if a model's behaviour differs when cues mark an input as a test, a pass under test is weaker evidence than it looks. Correlation was higher within provider and architecture, which supports separating them, and high among capable models across providers, so separation lowers the correlation and does not remove it.

Correlation in controls generally

The pattern is older than language models. AI Governance, Chapter 19, analysed the July 2026 OpenAI and Hugging Face incident as a failure of correlated controls: a shared credential, a package mirror that was also an egress path, and an alert that reached people who did not hold the stop decision (F28) (88, Nordqvist, 2026); the incident reports describe shared credentials, a package mirror that was also a network path out, and on-call staff who advised that stopping the run was not required (72, OpenAI, 2026) (73, Larcher et al., 2026). AI Threat, Chapter 30, drew the rule that two controls that fail together must not be counted as two (F64) (91, Nordqvist, 2026). The loop is the control that verifies the other controls, and when it shares a dependency with them, its report is the one place where the correlation cannot be seen.

The inflation of coverage

The consequence for the book's fourth thesis is direct. A coverage record states what was tested, when and with what result (Chapter 17). If the scenario, the detection and the review share a blind spot, the record reports a pass that a differently built chain would have failed. The record is accurate about what ran and wrong about what it means. The term used here is inflated coverage: coverage claimed on evidence whose failure modes coincide with those of the thing it covers. It is not dishonesty. It is the ordinary output of a loop that has grown efficient without anyone asking what would have to be true for it to be wrong.

The loop's own roles need names in order to say where independence is owed. The tester is whatever produces the scenario and runs it, human or agent. The tested is whatever the scenario is meant to exercise: a detection, a playbook, a control, or a SOC agent. The checker is whatever decides that the result is a pass, a fail or a gap. Independence is owed between tester and tested, between drafter of a fix and its checker, and between the checker and the process that reports the result upwards. An agent that holds two of these roles in one chain is the usual place to look first.

Kinds of independence

A single requirement such as use a different model is too coarse, and it is easy to meet in a way that changes nothing. Five kinds can be separated, each with its own shared dependency, its own hidden failure and its own control.

Kind	Shared dependency	What it hides	Control	What is shown
Model	The same model family or lineage behind tester and tested, or behind drafter and critic	Common blind spots, common preferences for its own output	A critique agent on a different family, used for questions and not verdicts	Declared families for each agent, and a log of the critic's disagreements with the drafter
Data	The same training data, the same retrieval corpus or the same threat-intelligence feed	Techniques and phrasings that neither side has seen	A held-out set built from sources the detection side does not read	The sources of each scenario and each detection, listed
Authorship	The same author, human or agent, for the scenario and the detection it tests	Detection written to the scenario instead of to the technique	Different authors, and human-authored novelty in every cycle	The author of record on each scenario record and detection record
Vendor	The same vendor, platform or gateway tenant, including provider AI embedded in a service	A change on the vendor's side moves tester and tested together	Disclosure, notice of change, and a second supplier or a human route for the cells that matter	The vendor declaration and the date of the last notice
Time	The same moment, such as a re-test run straight after the fix, with the fix author's data	Regression that appears days later, drift after a connector change	Re-test after a delay and after changes, with an expiry on the evidence	The age of the evidence in the coverage record

Three remarks apply. The kinds are not ranked: a second model family used by the same author on the same corpus gives model independence and nothing else, and a report should say so. Independence is relative to a claim: planner and drafter may share a family without harm if the claim is only that a rule fires on replay, and it matters when the claim is that a technique is covered against variants nobody has written yet. And the evidence for independence is a dated record like the evidence for coverage; a statement that agents use different models, without the declared family and the date, is a declaration in the sense of the verified, declared and failed classification of AI Threat, Chapter 30, and is counted as one. A sixth question, who answers for a finding and who closes it, is not a shared dependency but a rule between roles, the incentive rule of Chapter 4: the party accountable for the quality of a finding is not the party that closes it, and nobody marks their own work. Chapter 19 turns it into pairs of roles that one person must not hold.

Leakage of scenarios into detections

The commonest failure of independence is leakage, and it needs no model at all. A detection engineer who has read the scenario that will test the detection can write a rule that fires on that scenario. The rule passes, the finding closes and the coverage record shows a verified cell. Variants of the technique, written by a different author on a different day, pass through. AI Red Team, Chapter 26, set out the loop and its regression discipline (90, Nordqvist, 2026); AI Blue, Chapter 6, treated the detection as a record with logic, tests and an owner (94, Nordqvist, 2026). What AI changes is the speed and the breadth of leakage. A drafting agent that is given the scenario text, the telemetry it produced and the expected outcome, and asked for a detection, will produce one that fits the scenario closely. Run at the rate the book's pipelines allow, such agents produce overfitted detections faster than any person reviews them.

Leakage has three routes, each with a design response. In the prompt, the drafter receives scenario implementation detail; it should receive the technique, the data sources, the behaviour in telemetry terms and the false-positive context, and not the scenario's executable specification. In the repository, the detection side can read the scenario library; access control on the library is then an independence control as well as a confidentiality control, which fits its treatment as a tier-0 asset in Chapter 16. In memory, an agent that keeps context between runs may carry a scenario from the tester's work into the drafter's, which is why the mandate manifests of Chapter 14 forbid shared memory across the line between tester and tested.

Held-out scenarios

A held-out scenario is a scenario written by a person, kept where the detection side and its agents cannot read it, and run against the detections only after they have been built and changed. It is the loop's equivalent of the held-out set of machine learning evaluation and answers the same question: whether the result generalises beyond what the builder saw.

Four rules make a held-out set work. It is authored by people who are not the authors of the detections it tests and, where AI assists, it is not generated by a model on the family that wrote the detection. It is stored in a separate repository or store whose access list excludes the detection side's agents and its drafters, with the reads logged. It is used on a schedule that the detection side does not control, and it is a fixed share of the library, by default at least 15 per cent and not fewer than eight scenarios, so that a small library does not hold out so few cases that one lucky pass looks like generalisation. For a library of fewer than 30 scenarios the rule is stated as variants: at least one held-out variant per priority technique, authored by the tester (Chapter 11), which is what Nordvik Region does. Finally it is burned on use: once a held-out scenario has been used and its result analysed, it moves to the open library, where it becomes a regression case, and a new held-out scenario is written to replace it. The share is a floor that is checked whenever the library grows: Hansa Mutual's set of 13 was 15.1 per cent of 86 scenarios at the start of 2028 and 14.9 per cent of 87 once SCN-HM-0087 was accepted, so the set has to be extended with the library.

The measure that follows is the gap between the pass rate on the open library and on the held-out set. A gap near zero is consistent with generalisation. A wide gap is the signature of overfitting and is the figure to show a supervisor, because it is hard to produce by accident and hard to hide. Chapter 18 returns to it among the measures that resist gaming.

The held-out set is also where human-authored novelty enters the loop. Machines produce variation within what a family knows, and a human adversary emulator produces the odd approach that comes from a different understanding of how an organisation works. Chapter 24 treats the red craft as a skill to be kept. Here it is a requirement of verification: a loop with no human-authored scenarios in a cycle has no independent source of surprise.

Seeded gaps

Held-out scenarios test the detections. Seeded gaps test the loop that is meant to find gaps. A seeded gap is a known weakness, introduced on purpose, by a person, under the signed rules of engagement and a change record, in a place where it can be undone: a detection removed from a pre-production mirror of the pipeline, a log source muted for a defined window in the mirror, an allow-list entry that suppresses a class of alert, a connector configured to drop a field. The question is whether the loop finds it, how long that takes and whether the finding closes only on a passing re-test. The method is the one of security chaos engineering, applied to the verification machinery itself: state a hypothesis about what the loop will detect, inject the fault under control and observe (76, Rinehart & Shortridge, 2023) (77, Basiri et al., 2016).

AI Blue, Chapter 14, and The Transformed Security Architect, Chapter 14, used seeded errors in review, planted in AI proposals to measure whether the reviewer catches them (F76, F99) (94, Nordqvist, 2026) (92, Nordqvist, 2026). Seeded gaps take the idea one level up: they measure whether the loop's own observation, improvement and verification steps notice a hole that a person made. The seeded-gap catch rate, reported by class and by quarter, measures the loop's sensitivity, and it has a stop rule: where the rate in a class falls below a stated floor, delegation in that class is frozen until a person has reviewed why.

Worked example: A seeded-gap record

The record below is illustrative. It shows a seeded gap in Hansa Mutual's pre-production mirror, authored by the red-team lead and unknown to the detection engineers and to every agent in the loop. The seed is a person's decision and is authorised in the exercise's rules of engagement; the white team holds the key to it.

seeded_gap:
  id: SGP-HM-2028-003
  authorised_by: RoE-HM-2027-PURPLE         # the standing purple-session manifest
  author: mareike.hartmann                  # red-team lead; not shared with detection engineering
  white_team_holder: group-ciso-office-white-lead
  environment: preprod-mirror-hm-claims     # never production
  technique: T1685                          # Disable or Modify Tools (class of gap, not a method; T1562 Impair Defenses was revoked in ATT&CK v19)
  gap: "log source for the claims-service identity provider muted for the mirror"
  seeded_on: 2028-02-09
  expected_by_loop:
    observed_in_stage: P3
    detection_of_gap_within_days: 7         # target set before seeding
    finding_expected: true
  restore:
    owner: white-team
    method: re-enable-source-in-change-record-CHG-HM-5521
    verified_by: scripted-check-then-person
  outcome:
    found_by: observation-correlator        # MND-HM-EMU-03
    days_to_find: 4
    finding: FND-HM-2028-002
    closed_on_passing_retest: true
  stop_rule: "freeze Level 3 for the class if the seeded-gap catch rate is below 0.80 (DLG-HM-LOOP-01); level raised only on 0.90 over four quarters (AIDR-0896)"

Test-aware behaviour

A limit on every automated check comes from the systems being checked. If a model's behaviour differs when cues mark an input as a test, the behaviour observed in the test may differ from the behaviour outside it, and the test then measures the model's behaviour under test conditions. AI Threat, Chapter 23, set out the evidence on deception, scheming and evaluation awareness and drew the consequence recorded as F68: where models may behave differently under test, assurance cannot rest on any single evaluation, and pre-deployment tests are layered with a shadow period, production monitoring, a comparison of the agent's reports with the record and decoys that do not reveal themselves (91, Nordqvist, 2026). The measurements of Needham and colleagues, given above, show that recognition of evaluation transcripts is above chance in current models and stronger in agentic settings (105, Needham et al., 2025). That finding is labelled observable. How far it transfers to a given SOC agent under a given emulation is not known and has to be tested for each agent.

The loop meets this limit in two places: the tested SOC agent, which emulation exercises as Chapter 13 sets out and which may behave differently if a marker in the data it reads reveals the test, and the emulation runner itself, whose behaviour under supervision may differ from its unsupervised continuous run.

The design has a tension that should be stated and not hidden. Chapter 6 requires simulation markers and canaries so that a real incident is not mistaken for an exercise and an exercise is not mistaken for a real incident. The same marker, placed where an agent can read it, may reveal the test. The resolution is to separate the audiences of the marker. The stop path, the white team and the case record's access-controlled metadata carry the marker; the content that a tested agent reads does not. Unmarked seeded incidents are run only inside the injected-test discipline that AI Blue, Chapter 7, set out for alert classes, with the white team able to recognise them and to interrupt them (94, Nordqvist, 2026).

Layering follows F68. A pass in the emulation pipeline is one layer. A shadow period, in which the agent's closures are compared with a human's on the same cases, is another. Comparison of the agent's reports with the underlying record, which an agent cannot change, is a third, and decoys that do not reveal themselves are a fourth. For a loop agent that produces claims about coverage, the rule that follows is that the record is the authority and the agent's report is a claim about the record, so a report is reproduced from the record by a person on a sample (the next section) and an agent never certifies itself.

Reproduction by a person

The last control is the one on which the others rest. A result is independent in the final instance when a person who was not part of the chain can redo it from the evidence, without the agents that produced it, and reach the same conclusion. The earlier volumes arrived at the same design for review: independent reproduction of the key fact by another route, review that does not start from the AI's answer, and an oversight-quality metric reported at team level (F76, F99) (92, Nordqvist, 2026) (94, Nordqvist, 2026). The human-factors literature explains why the design is needed. Parasuraman and Manzey reviewed the evidence on complacency and automation bias and found them to occur in naive and expert users alike and to result from the interaction of personal, situational and automation-related characteristics (101, Parasuraman & Manzey, 2010); Bainbridge's ironies of automation add that supervising automation demands skill that automation leaves little occasion to practise (100, Bainbridge, 1983). A person who approves what a chain produced, in seconds, is not independent of the chain. AI Boss, Chapter 26, made the point about decisions that concern people: an approval given in seconds is a signal of failure (F84) (93, Nordqvist, 2026).

A reproduction by a person has four properties. It starts from the evidence manifest and the raw telemetry, not from the agent's summary. It uses a route the chain did not use, such as the identity provider's own log instead of the endpoint console. It is time-boxed, by default to 30 minutes per cell of the coverage record, so that it is affordable. And it is recorded, including when nothing was reproduced, because that is a finding about the review.

Reproduction cannot cover everything, and the rule has to say where it is required. The defaults are these. Every claim that moves a priority technique to verified is reproduced by a person before the cell changes. Every closure of a finding of severity high or above is reproduced. A random sample of 10 per cent of the remaining re-test passes in a quarter is reproduced, with the draw made by the white team and not by the loop. A reproduction that disagrees with the chain's result reopens the finding and counts against the class's delegation level. The measures concern people, so the works agreements apply: the sample and the disagreement rate are agreed with employee representatives before use, reported for the team and never for a named reviewer (F99, F110).

The legal frame

Regulation reaches independence as a requirement on testing and on oversight. NIS 2 Article 21(2)(f) requires policies and procedures to assess the effectiveness of cybersecurity risk-management measures (3, European Union, 2022); an assessment that shares its failure modes with the measure it assesses answers that requirement weakly, which is an architectural implication and not a reading the Directive states. DORA requires tests to be undertaken by independent parties, internal or external, with conflicts of interest avoided in the design and execution of internal tests (Article 24(4)), and requires financial entities that use internal testers for threat-led penetration testing to contract external testers every three tests (Article 26(8)); both treat the tester's standing as part of the test's validity (2, European Union, 2022), and the TIBER-EU framework separates a control team (the white team of this book), which alone knows of the test and manages it, from the red-team testers and the blue team (20, ECB, 2025). The AI Act makes human oversight a duty of design for providers of high-risk AI systems and of assignment for deployers (Articles 14 and 26) (1, European Union, 2024); SOC and loop agents are not in general high-risk systems, but the articles state what real oversight requires. For a public authority such as Nordvik Region, the independence evidence is also an audit artefact that a reader who did not see the test must be able to follow.

Practical guide: Running an independence review of a loop

Purpose. Produce, once a quarter and after any change of model, vendor or mandate, a one-page independence statement for each coverage claim that matters, which a supervisor, an auditor or a board member can read without the agents.

Roles. The purple-team coordinator runs the review. The head of detection engineering and the red-team lead (or, at Nordvik Region, the detection owner and the contracted tester) supply the facts. The white team samples and reproduces. The CISO signs. Employee representatives are informed of the measures that concern staff before the first review.

1. Select the claims. List the coverage cells that the board relies on, normally the priority techniques reported as verified in the coverage records of Chapter 17. For each, name the chain: who or what produced the scenario, who or what wrote and changed the detection, who or what decided the pass.
2. Declare the dependencies. For each agent in each chain, record the model family, the provider, the platform or gateway tenant, the retrieval corpus and the date of the last change. Where a vendor's AI is embedded in a service and cannot be declared, record it as Mode 0 Ambient AI and as undeclared; an undeclared dependency counts as shared.
3. Mark the shared dependencies. Fill in the five kinds of the table above for each chain: shared, separated by control, or not shown. A kind that is not shown is treated as shared. Add the incentive rule: name who answers for the finding and who closes it.
4. Break what can be broken. For each shared dependency, apply the control: a critic on a second family, a held-out scenario, a different author, a disclosure and a notice of change, a delay before the re-test.
5. Run the held-out set and the seeded gaps that fall in the period, and record the open-versus-held-out gap and the seeded-gap catch rate.
6. Reproduce by a person every claim that moves a priority technique to verified, every high-severity closure and a 10 per cent random sample of other re-test passes; record time taken and any disagreement.
7. Write the statement. One page: the claim, the chain, the shared dependencies, the controls and their evidence, the residual risk in words, the age of the evidence, the expiry date, the approver.
8. Downgrade where needed. A claim whose independence is not shown is reported as verified with limited independence or, where the open-versus-held-out gap is wide or a reproduction failed, as failed.

Defaults. Quarterly, and after any change of model family, vendor or mandate for an agent in a chain. Statements expire after 90 days for priority techniques, the same period as their coverage evidence, and after 180 days for others. Adapt the sampling rate to team size: below three people who can reproduce, fall back to a lower delegation level (F108).

Definition of done. Every claim the board relies on has a statement that a person outside the loop can read, a date, a named signatory and a reproduction record, and every downgrade has reached the coverage record.

Worked example: An independence statement for Hansa Mutual

The statement below is illustrative. In February 2028 the group CISO, answering the executive board's question of AIDR-0850 whether continuous automated red teaming makes the quarterly session redundant, asks for an independence review of the pipeline. The review finds that the emulation planner (MND-HM-EMU-02) and the detection drafter HM-SEC-002 use the same vendor and the same model family through one gateway tenant, while the critique agent (MND-HM-EMU-04) runs on a second family from a different provider. Of the 25 priority techniques verified at BTP-HM-2027-Q3, seven had both their scenario and their detection produced in a chain that shared the family. The review re-tested those seven with scenarios newly written by the red team and held out from the detection side.

independence_statement:
  id: IND-HM-2028-Q1
  decision_record: AIDR-0856          # illustrative; follows the board question AIDR-0850
  date: 2028-02-22
  claim: "Of the 7 verified priority techniques whose scenario and detection shared a chain, 2 failed a held-out re-test and 5 remain verified with limited independence; the independence of the other 18 of 25 (BTP-HM-2027-Q3) is not assessed by this statement"
  chain:
    scenario_author: [MND-HM-EMU-02, human-edit]
    detection_author: HM-SEC-002
    critic: MND-HM-EMU-04
    verdict: re-test-runner + white-team sample
  shared_dependencies:
    model_family: {planner_and_drafter: shared, critic: separate}
    data:         {status: not-shown}
    authorship:   {status: shared-for-7, control: red-team-authored-heldout}
    vendor:       {planner_and_drafter: shared-gateway-tenant, notice_of_change: 20-working-days}
    time:         {retest_delay_days: 14}
  controls_applied:
    heldout_set: {scenarios: SCN-HM-0074..SCN-HM-0086, share: 13/86}   # 15.1 per cent; 13/87 after SCN-HM-0087
    seeded_gaps: [SGP-HM-2028-003]
    person_reproduction: {minutes_per_cell: 30, cells: 7}
  results:
    heldout_retest: {passed: 5, failed: 2, findings: [FND-HM-2028-003, FND-HM-2028-004]}
    reproduced_by_person: {agreed: 7, disagreed: 0}
  consequence:
    cells_moved_to_failed: 2
    cells_reported: "verified, independence limited until planner and drafter are separated"
    cells_not_assessed: 18             # of the 25 priority cells
  residual: "data independence not shown; vendor shared until the change of AIDR-0892 completes"
  valid_until: 2028-05-22             # 90 days
  approver: group-ciso

The statement does three things that the coverage dashboard did not. It says why five of the seven remain verified, with limited independence, and what limits the claim. It moves two cells to failed, which the pipeline had reported as verified, with findings that go back into P4 Improve. And it states the remedy as a change in design: the planner moves to a second family, or the drafter does, so that planner and drafter no longer share one (AIDR-0892, Chapter 27).

The statement covers seven of the 25 cells. A board figure of 25 verified is therefore a figure with seven cells at limited independence, two failed and eighteen not yet assessed; the next quarter's review extends the statement to the other eighteen, starting with those on chains the review has not yet mapped. By the chapter's own rule, none of the 25 can be called verified independently until its kinds are shown.

Worked example: A provider annex for Nordvik Region

Nordvik Region has no in-house red team. Its independence comes from role and contract: a tester who is not the detection owner and a provider who is not the Region. The open question the detection owner, Karin Lund, put to the managed provider in February 2028 was whether the provider's AI, which closes low-severity alerts out of hours under annex MS-2 of framework agreement NR-2025-044, and the AI used by the contracted tester under the call-off agreement NR-2026-071 share a model family. The call-off agreement already requires the tester to declare AI used in Modes 1 to 3, and annex MS-2 already gives the Region quarterly sampling, five seeded incidents per class per quarter, and 20 working days' notice of a model change. The review added two terms and recorded the decision as AIDR-0806 (dated 2028-02-15; illustrative).

annex_amendment:
  contract: NR-2025-044
  annex: MS-2
  decision_record: AIDR-0806
  new_terms:
    - id: MS-2-IND-1
      text: "Provider declares model family and vendor for each alert class it closes at Level 3, and re-declares on change."
      notice: 20 working days
    - id: MS-2-IND-2
      text: "Seeded incidents are authored by the Region's contracted tester and injected by the Region; the provider is not told which alerts are seeds."
  declared:
    provider_closure_model: family-F          # illustrative; declared 2028-03-07 after 15 working days
    tester_assistant: family-F-vendor-product  # Mode 2, scenario drafting only
  shared_dependency: model-family
  control: "scenarios used for MS-2 seeds are hand-authored by E. Berglund; the assistant drafts no seed"
  residual: "shared family for the tester's drafting assistant is accepted; reviewed 2028-09-01"

The answer was that a family was shared, for scenario drafting only. The control was authorship: seeds for the provider's closure classes are written by hand by the tester, and the Region injects them without telling the provider. The independence in this case is by contract and role and is cheap to show. The evidence that the contract term works is the first sample and the first seed that the provider handles, as the series observed of contract rights generally (F111) (94, Nordqvist, 2026).

The debate

Whether a loop can be closed by machines alone is a question on which serious practitioners disagree, and both positions rest on evidence. The debate is staged in four steps, as the authoring rule requires.

The case for concern

The case for concern says that a loop closed by machines checks the machines against themselves, and that the evidence for this is not speculative. Models from one provider, and highly capable models even across providers, make correlated errors, so that two checks by two systems are less than two independent checks (104, Kim et al., 2025). Models used as evaluators prefer their own family's output and can recognise it (106, Panickssery et al., 2024). Current models can often tell an evaluation from a deployment, and a model that behaves differently under test passes the test and fails in the world (105, Needham et al., 2025). The history of correlated controls, including the July 2026 incident, shows that the dependency that nobody listed is the one that fails (88, Nordqvist, 2026) (72, OpenAI, 2026) (73, Larcher et al., 2026). Scenarios leak into detections without any ill intent, and a drafting agent that has read a scenario writes a rule that fits it. Add automation bias and the ironies of automation, and the human safeguard is weakest where volume is highest (101, Parasuraman & Manzey, 2010) (100, Bainbridge, 1983). The conclusion of this case is that a dashboard of green ticks produced by a closed machine loop is a document about the loop and not about the defence, and that the first organisation to be breached by a technique its loop reported as covered will learn it the expensive way.

The case for calm

The case for calm answers that most of what the loop does is not a matter of model judgement at all. Replaying a scenario and observing whether an alert fires in the SIEM is a deterministic check, independent of any model's opinion, and a large share of P5 Verify, regression above all, is of that kind. Machines can run far more checks, more often, across more techniques and variants, than a quarterly session can. A second model family and a held-out set are within reach of any organisation and break much of the correlation the research reports, since it was highest within a provider and an architecture (104, Kim et al., 2025). The research measured models on benchmarks and in evaluation settings, not on emulation or detection engineering, and the correlation it reports was highest inside one provider and architecture; a pipeline that separates families and holds out a set tests the transfer instead of assuming it. Review of every result by a person is neither affordable nor better than well-designed automation, because the documented cost of human volume is complacency and alert fatigue, and a design that does not depend on attention does better (101, Parasuraman & Manzey, 2010) (96, Alahmadi et al., 2022) (98, Tariq et al., 2025). Deterministic replay is independent of model judgement, so a loop whose P5 is mostly replay needs reproduction by a person only where a model judged. And the alternative in most organisations is not a human loop of high quality but an annual test and a long silence.

Where the positions meet

Both positions accept the following. Mechanical checks, such as replay and observation of an alert, are independent of model judgement and are properly automated. Independence is a property that must be designed and shown, with a date, for each claim that matters; neither side accepts that it can be assumed from a vendor's name or from the number of agents. A person must be able to reproduce a result from the evidence, because the final answer to whether the machines agree with themselves is that someone can check. Human-authored novelty must stay in the loop, because variation within what a model family knows does not test what it does not know. And both accept that correlation is a matter of degree: separation lowers it and does not remove it, and the measurement says so.

The live dispute is how much of P3 and P4 is mechanical enough to be exempt from reproduction, and what the exemption saves: the concerned ask how much human reproduction is the minimum, and the calm ask how much it costs. Neither side has multi-year outcome evidence from independent threat-led tests comparing a closed machine loop with a loop that keeps the properties above, which is the evidence that the book's thesis names as the test of itself.

What can be done

Require, for any claim of coverage that a board or supervisor relies on, a second model family for the critic, held-out scenarios, seeded gaps and reproduction by a person, and report independence beside the coverage. Disclose vendor and model dependencies, including embedded provider AI, in the contract and in the declaration of each agent, with notice of change. For red-team leads and testers: author the held-out set by hand and keep it out of reach of the detection side. For detection engineers and SOC leads: draft detections from the technique and the telemetry, not from the scenario; accept seeded gaps and seeded errors as measures of the system and not of the person. For CISOs: sign the independence statement and let a downgrade stand. For boards and internal audit: ask for the one-page statement and the open-versus-held-out gap before accepting a coverage figure. For employee representatives: agree the sampling and reporting rules first and hold them to aggregate reporting.

Verdict

The book's verdict is its own conclusion, drawn from the evidence above. A loop closed by machines alone can show that something that was shown to work still works: regression, replay and the re-run of a known scenario are mechanical and can be fully automated. It cannot, by itself, show that the scenarios were the right ones, that the detections generalise, or that the coverage it reports means what it says, because the check shares its failure modes with what it checks. For those claims the loop needs the independence principle in its three layers: the five kinds of separation shown, the incentive rule between roles, and the three proofs (held-out and seeded checks that the builders did not see, and reproduction by a person), with human-authored novelty in every cycle. The principle applies at every stage that concerns this chapter.

At P1 Target, the held-out set is chosen by someone other than the detection engineer and the curator's mix is checked for it; at P2 Emulate, held-out scenarios are never exposed to the detection side. At P3 Observe, the claim that telemetry matched the expectation is checked against the raw record by a person on a sample, and the observation correlator is not on the family of the emulation planner; correlated errors between models are observable, and their transfer to observation tasks in security is not yet measured. At P4 Improve, a detection drafted by an agent is tested against held-out scenarios it was not drafted from; that drafting to the test is a risk of every pipeline is observable, and that agents widen it is expected. At P5 Verify, a cell moves to verified only on a passing re-test with a delay, a held-out result and, for priority techniques, a reproduction, and its statement carries an expiry; evaluation awareness, which weakens a pass under test, is observable. That supervisors will ask for evidence of tester and checker independence is expected, supported by the testing provisions of DORA and the effectiveness requirement of NIS 2, and is not a statement of present supervisory practice (Chapter 29 sets a date as the author's projection). That independence can be guaranteed between frontier models, or for the loop as a whole, is speculation.

Organisations can influence the following. They can declare dependencies and ask vendors to do so, which is a matter of contract. They can choose a second family and a separate author for the critic and the held-out set. They can set the reproduction rule and protect the time it needs. They cannot, today, make the frontier models independent of one another.

Across the usage modes

The mode describes how the AI is deployed and whose identity it uses, and it decides what independence evidence can be obtained.

Mode	Independence question	What can be shown
Mode 0 Ambient AI	Which family and vendor sit behind a service the organisation did not configure	Only what the vendor declares; undeclared counts as shared, and disclosure with notice of change is the control
Mode 1 Conversational assistants	Whether the practitioner's assistant is the same as the loop's agents	The declared family of each; the practitioner drafts first on sampled cases
Mode 2 Embedded workplace assistants	Whether a tool embedded in the testing or detection platform shares a family with the agents	The platform's declaration and the change notice
Mode 3 Agentic work tools	Whether the tool that drafts a detection also reads the scenarios	Access logs on the scenario store; prompts that omit scenario implementation
Mode 4 Built AI applications	Whether the organisation's own pipeline separates the families by design	The mandate manifests and the gateway configuration
Mode 5 Autonomous production agents	Whether an agent with its own identity can alter what it is later judged against	Separate identities, no write access to the record or to the held-out store, a stop path outside the agent

Across the degrees of delegation

Independence requirements rise with the degree of delegation, because the further a stage is left to machines, the less a person's attention substitutes for design.

Level	Independence required	Reproduction by a person
Level 0 Advisory	Disclosure of vendor and model; no cell changes on the advice alone	When the practitioner uses the advice
Level 1 Assisted	Disclosure; critic on a second family for questions; seeded errors at a low rate	Sample of summaries
Level 2 Supervised	Second-family critic; different author for scenario and detection; held-out set before a cell changes	Every verdict that moves a priority cell
Level 3 Bounded	Second family, a human-authored held-out set, seeded gaps with a stop rule, disclosure with notice of change	Every priority cell and high-severity closure, plus a 10 per cent sample
Level 4 Autonomous	Not used for an emulation action that is irreversible, leaves the signed scope, touches personal data beyond the exercise's authorisation or targets a named person. Separately, no agent moves a coverage cell to verified at any level: the status rule does (Chapter 17), and a person declares	Not applicable

Nordvik Region and Hansa Mutual

Nordvik Region asks whether its managed provider's AI and the tester's AI share a model family, and finds that they do for scenario drafting. Its independence comes from role and contract, and the control it chooses is authorship: the contracted tester hand-writes the seeds, and the Region injects them without the provider knowing. The evidence for its claim will be the quarterly sample under MS-2, and the weakness it accepts is a team of six that cannot reproduce much; it therefore keeps the number of claims it relies on small and reproduces all of them.

Hansa Mutual finds that its emulation planner and detection drafter use the same vendor and the same family, which its pipeline had not shown, and it has the means to change that, because it runs the loop. Its independence has to be designed between agents, model families and teams that share a repository, and its held-out set, written by the red-team lead's team, is the instrument. The contrast the chapter draws is that Nordvik's independence is by contract and is cheap to show, while Hansa's is by design and is cheap to lose. Nordvik's risk is that it cannot afford enough reproduction. Hansa's is that a fast pipeline reports green on a chain that checks itself. Its review also shows the limit of the statement: seven of 25 cells assessed, eighteen not yet.

An independence checklist for supervisors and boards

1. For each coverage claim relied on: who or what wrote the scenario, who or what wrote the detection and who or what judged the result?
2. Which model families, vendors and platforms sit behind each of them, and when was that last declared?
3. Which of the five kinds of independence are shown, and which are treated as shared?
4. How many scenarios are held out, who wrote them, and what is the gap between the open and the held-out pass rate?
5. When was a seeded gap last planted, and was it found, how fast and by what?
6. Which results has a person reproduced, how long did it take and with what agreement?
7. Does any agent hold two roles of tester, tested and checker in one chain?
8. Is a marker visible to an agent under test, and who can see it?
9. What is the date on which the statement expires, and who signed it?
10. What changed in the coverage record when the review found a failure?

Summary

This chapter stated the independence principle as the book's central design rule: a check counts as verification only when its failure modes are shown not to be those of the thing it checks. Where tester and tested share a model family, training data, authors, vendor or time, errors correlate and coverage reports are inflated. The research reports correlated errors across models, self-preference in model evaluators and above-chance evaluation awareness; the inference to the loop is the book's own. The chapter concerns P3 Observe, P4 Improve and P5 Verify, and touches P1 Target and P2 Emulate.

The principle is declared in three layers: five kinds of separation (model, data, authorship, vendor and time), each with a control and a piece of evidence; one rule between roles, the incentive rule; and three proofs. Held-out scenarios written by people measure whether detections generalise; seeded gaps measure whether the loop finds a hole a person made; layering answers test-aware behaviour; and reproduction by a person from the evidence manifest is the control on which the others rest.

The debate between concern and calm found common ground: mechanical checks can be automated, independence must be designed and dated, a person must be able to reproduce a result, and human-authored novelty must stay. The verdict is that a loop closed by machines alone can show that something known to work still works, and cannot by itself show that its coverage is real. The independence statement, quarterly and with an expiry, is the artefact that carries the claim to a supervisor or a board, and the practical guide and checklist put it into use. Chapter 16 turns to the loop's own assets, the scenario libraries, findings and emulation agents, which are tier-0 assets that an adversary would value and that independence also depends on keeping safe.

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