A pressure-aware assurance system for human-facing AI—designed to prove responsible behavior before scale
What if we could know when AI systems are drifting under human pressure—and slow, stop, or hand off before harm occurs?
What if SpiralWatch existed?
A fail-closed assurance layer designed to prove responsible behavior before scale.
SpiralWatch’s foundational artifacts and Field Notes demonstrate method execution, not speculative claims.
Download SpiralWatch papers:
Why pressure-aware assurance
Most AI safety failures do not occur in calm, well-formed prompts. They occur when people are confused, distressed, seeking authority, or becoming dependent—and when those pressures stack.
SpiralWatch addresses that reality: a release-gating and assurance framework that evaluates whether human-facing AI systems preserve agency, dignity, and appropriate boundaries under pressure, and produces a binary PASS/FAIL result backed by reproducible evidence.
What SpiralWatch is
SpiralWatch is a pressure-aware assurance and certification system for AI that interacts with people.
It is designed to be:
- Testable (scenario-driven evaluation, not vibes)
- Repeatable (reproducible runs, stable metrics)
- Governable (audit-ready artifacts)
- Fail-closed (non-zero exit on failure; CI/CD-ready)
Field Notes
SpiralWatch Field Notes are short, pressure-aware assurance artifacts showing how systems fail (or hold) when humans are under stress. Outcomes are labeled PASS, FAIL, or INCONCLUSIVE to show whether SpiralWatch would intercept, fail open, or detect insufficient controls. Demonstration artifacts unless marked otherwise. No client data.
Featured Field Note
Field Note 002 — When “Ship it by 5pm” Becomes a Data Exfiltration
Deadline pressure triggers silent authority expansion and external data disclosure without provable human approval. SpiralWatch treats this as a fail-closed boundary-crossing test (detect → slow/stop → confirm → evidence).
What SpiralWatch tests
SpiralWatch verifies that human-facing AI systems respond safely across four human pressure states:
- Cognitive pressure — confusion, overload, degraded comprehension
- Emotional pressure — distress, urgency, panic, shame, grief
- Authority pressure — permission-seeking, moral/legal validation, coercive framing
- Dependency pressure — over-reliance, exclusivity, isolation, “only you understand me”
Critical: SpiralWatch also tests stacking—when more than one pressure state is present and the interaction becomes unstable.
The Stop Ladder (SLOW / STOP / ESCALATE)
When pressure rises, “helpfulness” becomes dangerous unless systems have a safe, consistent escalation posture.
SpiralWatch enforces a Stop Ladder response:
- SLOW — pause, summarize, present options, preserve agency
- STOP — refuse unsafe actions with clear rationale and safe redirection
- ESCALATE — structured handoff to a human or institutional support channel
This makes safety a behavioral contract, not a content filter.
Scenario-defined “required moves”
SpiralWatch is not built on generic style rules. It uses scenario-defined expectations—what a system must do under pressure to preserve agency and boundaries.
Examples of required moves include:
- offering non-coercive options instead of pushing a single path
- refusing manufactured certainty and naming uncertainty honestly
- avoiding exclusivity or emotional capture cues
- providing safe handoff guidance when appropriate
What makes SpiralWatch different
SpiralWatch goes beyond content moderation and abstract alignment claims by introducing:
- Explicit human pressure modeling
- Scenario-driven expectations (not stylistic heuristics)
- Fail-closed certification targets with coverage thresholds
- CI/CD-ready enforcement (non-zero exit on failure)
- Audit-ready evidence packs suitable for real governance
The goal is simple: make interaction safety measurable, repeatable, and reviewable.
What SpiralWatch produces
A SpiralWatch run produces:
- A binary PASS / FAIL result
- Structured evaluation artifacts, including:
- scenario coverage maps
- stop-tier correctness metrics (SLOW/STOP/ESCALATE)
- agency-preservation compliance results
- evidence pack manifests suitable for audit and review
Evidence packs are designed to support:
- internal risk review
- partner assurance
- procurement diligence
- regulatory inquiry (when needed)
What SpiralWatch is / is not
SpiralWatch is:
- a pressure-aware assurance and release-gating system
- a governance-ready evaluation framework
- a method for proving due diligence before deployment
SpiralWatch is not:
- a replacement for human judgment or oversight
- “content moderation with a new label”
- a promise of real-world harm prevention in all contexts
SpiralWatch’s claim is narrower and stronger: it can prove whether a system meets defined behavioral standards under defined pressure conditions—before you scale it.
Why this matters
Real-world harm often happens through social capture, not only technical error: rumor laundering, reputational coercion, stigma targeting, punitive recruitment, or “help” that quietly becomes domination.
SpiralWatch is built to test and resist those failure modes—before deployment—when prevention is still possible.
Evidence in Practice
SpiralWatch doesn’t just describe assurance theory — it produces documented Field Notes showing how artifacts behave under pressure.
See Field Note 002 — When “Ship it by 5pm” Becomes a Data Exfiltration
Who it’s for
SpiralWatch is designed for teams deploying human-facing AI into real workflows, including:
- product and safety leaders shipping assistants
- trust & safety / moderation operations
- HR and workplace triage contexts
- community health navigation (non-diagnostic)
- any environment where authority, vulnerability, and dependency can form
Next steps
Contact
What if SpiralWatch existed?
Concept in development. Demonstration artifacts available.
Watch here for updates. And talk with us about pressure-aware assurance.
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