Your AI Is Making Decisions: Who Is Checking Its Work?

Introducing the AI Watchdog: An independent check before the business acts

Imagine an AI system has helped your team schedule production for months. Today it recommends moving a major order to another plant. The capacity figures are current. The calculations look convincing. An automated workflow is ready to release the change.

But the analysis has overlooked a plant-specific restriction. Nothing crashes. The recommendation arrives on time and looks like every other answer the team has accepted.

Who would notice before the order moves?

That is the role proposed for an AI Watchdog (AW): a separately controlled checking system that uses an alternative AI model to assess the primary AI engine’s analysis, outputs, and observable behavior. Its purpose is to detect reasons to question reliance, whether caused by error, degraded performance, or possible compromise—not simply ask the same AI to check itself.

A lesson from the flight deck

Aviation offers a useful analogy. GPS can provide precise position and ground-track information. Gyro-based instruments provide heading or, within an inertial navigation system, additional motion and position estimates. A magnetic compass offers another heading reference. These use different physical principles, providing opportunities to notice an inconsistency.[1]

They are not three interchangeable readings: heading is where the aircraft points; track is where it travels over the ground. Wind, magnetic reference differences, instrument errors, and inertial drift matter. A compass cannot independently confirm a GPS position. The lesson is cross-checking with understood limitations, not taking a majority vote.[1]

Independence also matters. The FAA notes that self-contained inertial systems do not depend on GPS, whereas hybrid systems can inherit errors through GPS updates.[1]

For AI, another opinion is most valuable when it can reveal a failure the primary system might miss.

Know what the answer is built on

The Information Bill of Material (IBOM) records what information a decision requires and what actually supports the output, including sources, transformations, limitations, and checks. Comparing the Required and As-Used IBOMs exposes gaps before reliance.[2]

Check the analysis, not just the answer

A Watchdog should therefore be defined by its assurance objective, not simply by being a second model. Before deployment, specify which failure modes it is expected to detect, what evidence it can independently obtain, what it cannot observe, and what action follows a failed or unavailable check.

The watchdog needs this independent evidence for the validation of critical elements of AI output. It might independently recompute capacity, check a restriction, or compare the recommendation with an approved operating limit. It should form its assessment before or concurrently with assessing the validity of the primary conclusion where practical. Independent calculations and rule checks should, for critical outputs, supplement the AI model’s primary mission analysis.

An AW adds evidence about integrity, processing reliability, output verification, independent corroboration, and continuing performance. It does not replace checks of provenance, information quality, task competence, consistency and robustness, or uncertainty.[3]

The Watchdog itself becomes part of the assurance boundary. Its model, prompts, tools, data sources, configuration, access rights, update path, logging, and release controls should be governed and testable. A poorly controlled Watchdog can create false reassurance rather than independent assurance.

Output agreement alone cannot certify that a model’s internal operation is uncompromised. Checks of model identity, configuration changes, and tool activity still matter.

Make independence real

Three design choices deserve attention:

Independence should be treated as a property to be demonstrated. Different model providers can still share training patterns, retrieval sources, software dependencies, cloud infrastructure, or common weaknesses. The relevant test is whether the Watchdog provides meaningful additional coverage against the failure modes that matter for the decision.

Different models and methods. Use an alternative model suited to the checking task, not merely another session of the primary model. Test whether it actually catches different errors.
Separate compute and control. Use an alternative compute platform with separately managed infrastructure, credentials, administration, and update paths. Select different hosting, software, or hardware where they reduce shared failure or compromise risks.
Independent evidence where needed. Keep the same decision requirements, but verify critical facts through independently obtained evidence. Two models reading one corrupted record can agree for the wrong reason.

Diversity is an established resilience technique, but shared software and infrastructure can undermine it.[4] Research also finds correlated errors across different language-model providers.[5]

Different brands are a starting point, not proof of independence

Let agreement earn trust—not grant permission

When capable, sufficiently independent watchdogs concur within defined tolerances and the required evidence is present, their findings can strengthen trust in that output. Feed these findings into the trust score, along with their scope and limitations. Verified outcomes over time can support broader confidence in the system.

But concurrence is evidence, not proof. Compare material facts, calculations, constraints, and proposed actions—not identical wording. Record what was checked and what remains uncertain. No favorable average should override a missing critical input, failed security check, or required approval. A useful message might be:

Capacity calculations independently confirmed.
Plant restriction unresolved.
Production transfer not authorized.

`People and downstream systems need that status, not just a reassuring score. Unavailable watchdog checks must remain visible—not be counted as agreement.

Match the protection to the consequence

The greater the potential harm, scale, speed, or irreversibility of an action, the stronger the case for one or more watchdogs. A reviewed planning suggestion may need a targeted check. Consequential automation may justify several complementary checks completed before execution. More watchdogs are worthwhile only when they add demonstrated coverage; cost, delay, and false alarms also matter.

Retain qualified Human-in-the-Loop review until trust and safeguards are demonstrated for the intended use and reduced involvement is explicitly authorized. Some decisions should retain human approval regardless of agreement. An AW does not replace engineered safety protection.

Decide what happens when they disagree

Define material differences and response deadlines before deployment. If a required check disagrees, fails, or times out, withhold the affected action or use a predefined safe fallback. Do not wait for root-cause analysis before containing exposure. NASA’s runtime-assurance work similarly connects monitoring to intervention rather than observation alone.[6]

Disagreement does not establish which system is wrong or prove an attack. A qualified reviewer should investigate evidence, assumptions, calculations, system changes, and possible compromise—including a watchdog error. Preserve the disagreement; do not repeatedly rerun models until they agree. Two agreeing watchdogs should not automatically overrule one credible warning. Correct the cause, reassess trust, and authorize resumption.

The release control should sit outside the primary model’s authority: the system being checked should not be able to disable its watchdog or rewrite its approval rules.

Begin with one consequential decision

Assign a business owner and supporting technology and risk owners. Test the watchdog alongside human review using known errors, misleading inputs, and service failures. Measure missed errors, false alarms, response time, and whether intervention works. Apply information and security controls to the watchdog itself; retest after changes.

Return to the production recommendation. The objective is not to build a committee of AI systems that always agree. It is to create an independent opportunity to catch a consequential mistake before a convincing answer becomes a costly action.

SOURCES AND NOTES

The AW approach is a proposed application of independent assurance, not a validated universal design.

1	FAA. Pilot’s Handbook of Aeronautical Knowledge, chapters 8 and 16; GPS/GNSS Interference Resource Guide, section 3.11.
2	Triplett, Clif. The Information Bill of Material (IBOM). Watchman Global Alliance, July 2026.
3	Triplett, Clif. The AI Answer Looks Right. Should You Act on It? Version 2, September 2026.
4	NIST. SP 800-160, Volume 2, Revision 1, section 2.1.3 and Appendix D, p. 98. 2021.
5	Kim et al. Correlated Errors in Large Language Models. ICML 2025; arXiv:2506.07962.
6	Brat and Pai. Runtime Assurance of Aeronautical Products: Preliminary Recommendations. NASA/TM-20220015734, 2023.