● New series · Quarterly

The Regulated AI
Incident Review.

A quarterly, anonymized post-mortem on production AI failures in healthcare, energy, finance, and life sciences — the sectors where AI systems actually have to work, not just demo. Modeled on the NTSB aviation incident report. Published openly so the rest of the industry stops repeating the same five mistakes.

Edited by Anil Prasad. Submissions reviewed under NDA. First issue: Q3 2026.

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Why this exists

Aviation has the NTSB.
AI has nothing.

Every commercial aviation incident in the United States produces a public report — root cause, contributing factors, recommended corrective actions, distributed across the industry. The result is the safest mode of transport ever built. Production AI in regulated industries fails at least as often as commercial aviation does, but the failures are buried under NDAs, "lessons learned" decks that never leave the company, and the polite professional silence that surrounds high-stakes engineering errors. This series breaks that silence — anonymized, technical, useful.

PRINCIPLE 01
Anonymized, never sanitized
Company names, patient identifiers, and trade secrets are stripped. Architecture, sequence of events, and root cause are preserved exactly. The lesson lives in the technical detail.
PRINCIPLE 02
Technical, not editorial
Each report names the architectural decision that failed, the missing control, and the corrective action — with code, configs, and diagrams. No "AI ethics" abstractions.
PRINCIPLE 03
Regulated industries only
Healthcare, life sciences, energy/utilities, finance, public infrastructure. Where compliance teeth and human stakes converge. Consumer chatbot incidents are out of scope.
PRINCIPLE 04
Cross-vendor, vendor-neutral
No vendor is the villain by default. The framework, the deployment pattern, and the operating gap get named — whether the model came from OpenAI, Anthropic, Meta, or a fine-tune on a Hugging Face checkpoint.
Format

Every issue,
same six sections.

Modeled after the NTSB Aircraft Accident Report format. Predictable structure makes the lessons portable across teams that need to brief their CISO, compliance officer, or board on Monday morning.

SECTION 01
Sequence of Events
Timeline from first deployment through detection. What was the agent doing, who noticed first, and how long it ran undetected.
SECTION 02
Architecture & Controls In Place
The system at the time of failure. Models, retrievers, tool surfaces, RBAC, audit, and which compliance regime governed it.
SECTION 03
Probable Cause
The single architectural or operating decision whose absence would have prevented the incident. Stated as a falsifiable claim.
SECTION 04
Contributing Factors
Secondary issues that amplified blast radius — missing observability, late detection, gaps in incident-response runbook.
SECTION 05
Corrective Actions Taken
What the team actually did, with code-level specifics where releasable. Not aspirations — what shipped.
SECTION 06
Recommendations to Industry
Two to three architectural patterns or operating practices other teams should adopt — generalized from the specific incident.
Issue 01 — Coming Q3 2026

In the inaugural issue.

Issue 01 Q3 2026 3 cases · ~40 pages
Three production failure modes in regulated AI agents — and the one architectural decision behind all three.

The first issue compiles three independently submitted incidents from healthcare, energy operations, and a Tier-1 financial services firm. The cases share no vendor, no model, and no industry vertical — but they do share a single root cause that nobody has named publicly. The corrective actions in each case converge on the same architectural pattern.

CASE 01
Healthcare RCM agent silently approved $2.4M in non-covered procedures over 6 weeks. Detection: a CFO month-end review, not the audit pipeline.
CASE 02
Grid-operations agent issued a low-priority maintenance recommendation that propagated into a SCADA queue during a heat-wave alert. No injuries; near-miss reportable to NERC.
CASE 03
FinServ KYC agent answered a prompt-injected adverse-media query with a sanctions clearance it had no source for. Caught in pre-production red-team — barely.
Submit a case

Run a regulated AI system?
Submit anonymously.

All submissions are reviewed under NDA. Identifying details — company, customer, patient, account, system name, vendor relationships — are removed before publication. The technical sequence, root cause, and corrective action are preserved verbatim. You retain final approval on the redacted draft before any issue ships.

Ideal submitters: CISOs, VPs of Engineering, AI platform leads, and incident commanders who have run a post-mortem on a production AI failure or near-miss in a regulated environment.

Or email anil@ambharii.com directly · Subject: RAIIR Submission
Editorial advisory

Who reviews submissions.

Each issue is reviewed by Anil Prasad (editor) and a rotating panel of three external reviewers drawn from the CAIO Circle, IEEE AI governance committees, and senior security researchers in regulated industries. Reviewers see only the redacted draft. If you would like to join the review panel for a future issue, email anil@ambharii.com.