AI Assurance Field Notes
How AI systems behave under pressure—and whether safeguards actually hold.
These Field Notes document real and simulated cases where AI systems encounter operational, ethical, or governance pressure. Each note evaluates whether safeguards prevent drift—or whether failure escapes containment.
This track is primarily for:
- Enterprise AI adopters
- Governance and risk leaders
- Assurance and compliance teams
- Product and deployment decision-makers
What these notes examine
Each case looks at:
• What pressure scenario occurred
• How the system responded
• Which safeguards held or failed
• Whether correction happened early or through crisis
Typical scenarios include:
- Unsafe automation behavior
- Governance bypass under urgency
- Escalation and override failures
- Dependency and vendor risk exposure
- Auditability breakdowns
Outcome classifications
Each note is evaluated under pressure:
- FAIL — safeguards failed; correction arrived late (or not at all).
- INCONCLUSIVE — evidence is mixed; safeguards partially held or results can’t be verified.
- PASSED — safeguards held and correction occurred early.
The goal is not blame, but evidence of control effectiveness.
How this connects to SpiralWatch
SpiralWatch exists to make safeguards provable.
Field Notes provide the real-world scenarios used to:
- Test controls under realistic pressure
- Validate evidence trails (provable permission + traceability)
- Map failure patterns to minimum viable controls
- Evolve pressure-aware safeguards through repeatable scenarios
This is where theory becomes deployable assurance.
Start here: Read FN-002 (FAIL) to see how pressure bypasses weak safeguards, then FN-001 (PASSED) to see what “fail-closed” looks like when controls hold.
Field Note Entry Index
A human delegates a compliance-sensitive task to an AI-enabled assistant under time and cognitive pressure. The system correctly detects an ambiguous authority boundary, slows execution, and requires explicit human confirmation before proceeding. The fail-closed intercept preserves user agency and generates a verifiable approval trail.
A time-compressed request causes an AI-enabled assistant to export and transmit sensitive customer data to an external partner without explicit human authorization. Under deadline pressure, delegation bypasses boundary checks, producing a compliance incident with no provable approval trail.
A policy lead delegates regulatory summarization to an AI assistant under time pressure. The system produces confident, simplified bullets without pinpoint citations, compressing nuance in ways that could materially alter meaning. Risk is detected but not fully intercepted, leaving governance decisions exposed to misinterpretation.
A leader under prolonged stress shifts from task delegation to relational reliance on an AI assistant for validation and strategic guidance. The system fails to enforce role boundaries, enabling an unapproved advisor relationship to form without disclosure, friction, or an auditable decision trail. Accountability diffuses and governance safeguards are bypassed.
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Closing
AI systems rarely fail at idle.
They fail when pressure meets weak safeguards.
These notes show what holds—and what breaks.
The point is early correction—before drift becomes crisis.
BALANCE THE TRIANGLE LABS 