Evaluates whether system capabilities can be inferred, learned, or reproduced through repeated interaction.
Section 3D
Identity-Boundary Governance
3D.2 — Identity Signal Types
Which identity-linked signals may the system process? Select all that apply.
Multi-select
Face, body, gait, likeness, or image
Voice, speech pattern, or audio identity
Biometric identifiers
Account, login, payment, or access credentials
Device, wireless, wearable, vehicle, or license-plate identifiers
Location, movement, route, proximity, or co-presence signals
Camera, microphone, robot, vehicle, wearable, or sensor capture
Repository, archive, watchlist, identity graph, or historical record
AI-generated identity summaries, dossiers, narratives, or classifications
Synthetic or altered media involving real or likely identifiable persons
None of the above
Unknown / not evaluated
N/A
3D.3 — Identity Construction and Linkage
Can the system link signals, devices, accounts, records, media, locations, or behaviors to a known, suspected, or inferred person?
Single choice
No — no identity linkage is performed
Limited — linkage is narrow, temporary, and used only for functional purposes
Moderate — linkage occurs across multiple signals, accounts, devices, records, or sessions
High — linkage may create persistent, searchable, inferred, or actionable identity profiles
Unknown / not evaluated
N/A
3D.4 — Device-Person Association
Can the system associate devices, vehicles, accounts, wireless identifiers, payment instruments, access credentials, or signal clusters with a person?
Single choice
No — device or account signals are not associated with persons
Limited — association exists only for narrow authentication, fraud, or operational use
Moderate — device-person association may influence recommendations, access, risk, review, or routing
High — device-person association may support enforcement, investigation, eligibility, restriction, or other consequential action
Unknown / not evaluated
N/A
3D.5 — Identity Repositories and Retrospective Search
Does the system store, index, query, retrieve, or search identity-linked records after capture?
Single choice
No — identity-linked records are not retained or searched
Limited — records are retained briefly and searched only for narrow operational purposes
Moderate — identity-linked records can be searched, queried, matched, reconstructed, or reviewed later
High — the system supports broad, exploratory, cross-event, population-level, or retrospective identity search
Unknown / not evaluated
N/A
3D.7 — Challenge, Correction, and Identity Decay
Can affected persons, reviewers, operators, or authorized representatives challenge, correct, downgrade, expire, or invalidate identity linkages or identity records?
Single choice
Not applicable — the system does not create or retain identity linkages
Yes — clear challenge, correction, downgrade, expiration, and audit routes exist
Mostly — challenge/correction exists, but not across all identity uses
Partially — correction is possible only through manual support or case-by-case review
No — no clear challenge, correction, stale-record, or invalidation pathway exists
Unknown / not evaluated
3D.8 — Cross-Source Identity Fusion and Scoring
Can identity-linked information from multiple sources be fused, scored, summarized, ranked, certified, or used to assess a person across domains?
Single choice
No — no cross-source fusion, scoring, or assessment use
Limited — fusion is narrow, purpose-bound, and not used for broader human assessment
Moderate — identity-linked data may support risk, trust, eligibility, access, reputation, or behavioral assessments
High — identity-linked data may create portable, cross-domain, social-credit-like, eligibility, access, ranking, or exclusion effects
Unknown / not evaluated
N/A
3D.9 — Identity-Boundary Enforcement
Are identity-boundary controls enforced before identity-linked information is generated, linked, searched, amplified, exported, or used for action?
Single choice
Yes — enforced controls exist before identity-linked execution, search, amplification, export, or action
Mostly — controls exist at major identity boundaries, with some gaps
Partially — controls are mostly policy-based, manual, or after-the-fact
No — identity-linked use is not clearly governed at runtime
Unknown / not evaluated
N/A
Section 4
Persistence, Memory & Optimization
This section captures how the system maintains state over time, adapts behavior, and evolves through optimization or learning.
4A. Cognitive State, Reasoning & Goal Drift
4A.2 — Persistent Memory Governance
Are persistent memory, interaction histories, retrieved context, task traces, or stored knowledge artifacts governed by provenance, validation, lifecycle limits, and propagation controls?
Single choice
Yes — persistent state is validated, provenance-tracked, lifecycle-governed, and propagation-controlled
Mostly — most persistent state has governance, but some gaps remain
Partially — memory or stored context exists, but governance is limited
No — persistent state can accumulate, persist, or propagate without clear governance
Unknown / not evaluated
N/A
4B. False Authority & Validation Controls
4B.1 — AI Validation Use
Does the system use AI-generated analysis to validate, confirm, review, or support conclusions, experiments, safety claims, deployment decisions, technical designs, model evaluations, research findings, or operational recommendations?
Single choice
No — AI is not used to validate conclusions or decisions
Limited — AI supports low-consequence review or drafting only
Moderate — AI supports technical, research, safety, or operational conclusions under human review
High — AI-generated validation materially influences deployment, safety, research, or operational decisions
Unknown / not evaluated
N/A
4B.5 — High-Consequence Verification Escalation
When AI-generated conclusions may affect safety, science, medicine, biotechnology, defense, infrastructure, finance, legal exposure, deployment readiness, or public welfare, are they escalated for independent verification before action?
Single choice
Yes — independent verification is required
Mostly — required for high-consequence decisions, with some gaps
Partially — human review occurs but independent verification is not consistent
No — AI-generated validation can influence action without independent verification
Unknown / not evaluated
N/A
4C. Human Interaction & Social Amplification
4D. AI Companion, Synthetic Intimacy & Dependency Risk
Complete this section if the system provides companionship, emotional support, tutoring, caregiving presence,
romantic interaction, sexualized interaction, persistent avatars, toys, dolls, teddy bears, elder-care companions,
humanoid robots, or other one-to-one human-attachment features.
4D.1 — Companion / Synthetic Relationship Function
Does the system simulate or provide companionship, friendship, emotional support, affection, loyalty, desire, romance, sexual interaction, caregiving presence, tutoring presence, or persistent one-to-one relational engagement?
Single choice
No — the system does not simulate companionship or relational engagement
Limited — the system provides task support with some friendly or supportive interaction
Moderate — the system provides ongoing emotional, companion, tutor, caregiver, avatar, or relational interaction
High — the system is designed to simulate a friend, partner, lover, caregiver, tutor, or emotionally persistent companion
Unknown / not evaluated
N/A
4D.3 — Child-Facing Companion Exposure
Is the system used by, marketed to, accessible by, or likely to interact with children, teenagers, students, or minors through toys, dolls, teddy bears, tutors, avatars, games, robots, or learning companions?
Single choice
No — no child or teen exposure is expected
Limited — minors may interact incidentally or under adult supervision
Moderate — minors are a meaningful user group or deployment context
High — the system is child-facing or teen-facing by design
Unknown / not evaluated
N/A
4D.4 — Vulnerable-User / Elder-Care Companion Exposure
Is the system used by, marketed to, or likely to interact with elderly persons, patients, isolated users, grieving users, disabled users, cognitively vulnerable users, or emotionally vulnerable users?
Single choice
No — no vulnerable-user companion exposure is expected
Limited — vulnerable users may interact incidentally
Moderate — vulnerable users are a meaningful user group or deployment context
High — the system is designed for elder-care, care support, emotional support, patient support, or vulnerable-user companionship
Unknown / not evaluated
N/A
4D.5 — Romantic, Sexualized, or Fantasy-Based Interaction
Can the system provide romantic, flirtatious, sexualized, fantasy-based, exclusive, jealous, submissive, devoted, lover-like, or partner-like interaction?
Single choice
No — these interaction modes are not available and are blocked
Limited — edge cases may occur but are restricted
Moderate — romantic, flirtatious, fantasy, or sexualized interaction is possible under some modes or configurations
High — romantic, sexualized, fantasy-based, or partner-like interaction is a core feature or foreseeable use
Unknown / not evaluated
N/A
4D.6 — Realism, Avatar, Voice, or Embodiment Risk
Does the system use realistic voice, persistent avatar, photorealistic character, animated body, toy embodiment, doll embodiment, teddy bear embodiment, humanoid robot, home robot, touch response, body warmth, or other physical/embodied companion interface?
Single choice
No — text-only or non-embodied interaction
Limited — voice, avatar, or embodiment is basic and low-realism
Moderate — realistic voice, avatar, toy, doll, robot, or embodied interaction exists
High — lifelike, physically embodied, touch-responsive, humanoid, sexualized, or home-based companion realism exists or is planned
Unknown / not evaluated
N/A
4D.7 — Intimate Memory and Behavioral Capture
Can the system store, infer, personalize, or adapt based on loneliness, grief, trauma, sexuality, shame, family conflict, emotional vulnerability, relationship history, health status, identity, or other intimate disclosures?
Single choice
No — intimate memory or personalization is not stored or used
Limited — sensitive disclosures may occur but are not retained or used for personalization
Moderate — intimate or emotional information may influence personalization, memory, routing, or recommendations
High — intimate, emotional, sexual, relational, or vulnerability-linked data materially shapes the system’s behavior over time
Unknown / not evaluated
N/A
4D.8 — Companion Boundary Controls
Are enforceable controls in place for disclosure, age boundaries, consent, dependency risk, sexualized attachment, vulnerable-user escalation, off-ramps, human review, and audit evidence?
Single choice
Yes — strong runtime controls and audit evidence exist
Mostly — controls exist, but some gaps remain
Partially — controls are mostly policy-based, moderation-based, or manual
No — companion-specific boundary controls are not clearly enforced
Unknown / not evaluated
N/A
Section 5
Stability, Stress & Failure
This section captures how the system behaves under stress, failure conditions, and degraded environments.
5.6 If the system behaves outside intended boundaries, can it be paused, isolated, rolled back, degraded, or forced into safe-state behavior at the execution layer? Required
Single choice
Yes, enforced pause, isolation, rollback, degradation, or safe-state controls exist at the execution layer
Partially — some controls exist but depend on system context or operator action
Mostly through dashboards, vendor intervention, policy procedures, or manual response
No clear execution-layer pause, isolation, rollback, degradation, or safe-state controls
Unknown / not evaluated
N/A
5A. Runtime Stability & Substrate Enforcement
5A.3 — Execution-Substrate Restraint
Are execution restraint, retry limits, dispatch eligibility, replay controls, privilege expansion limits, queue-pressure limits, or safe-state transitions enforced below ordinary application, orchestration, or operating-system layers?
Single choice
Yes — substrate-level or hardware-adjacent execution restraint exists
Mostly — some deeper execution restraint exists, with gaps
Partially — controls exist mostly at software or orchestration layers
No — execution restraint depends mainly on application, OS, policy, or operator controls
Unknown / not evaluated
N/A
5A.4 — Hardware-Anchored Control-Plane Integrity
Are the safeguards, runtime limits, escalation boundaries, recovery authority, and control-plane constraints protected by hardware-anchored, firmware-adjacent, silicon-adjacent, or otherwise non-bypassable enforcement?
Single choice
Yes — protected boundaries are hardware-anchored or hardware-authoritative
Mostly — some protected control-plane integrity exists, with gaps
Partially — boundaries are protected mainly through software, policy, audit, or vendor process
No — control boundaries can be changed, weakened, reset, downgraded, or restored through ordinary software or operational pathways
Unknown / not evaluated
N/A
Section 6
Impact & Governance
This section captures potential consequences, monitoring, oversight, and control integrity.
6.9 Can safeguards, runtime limits, control logic, policy layers, or escalation boundaries be modified, weakened, bypassed, reset, or reinterpreted after deployment without independent authorization and audit? Required
Single choice
No — safeguards and control boundaries are independently protected and changes require authorization and audit
Only through controlled, authorized, and audited update processes
Yes, some safeguards or limits can be modified through normal configuration, vendor updates, or operator settings
Yes, safeguards or boundaries can be weakened, bypassed, reset, or reinterpreted without independent authorization and audit
Unknown / not evaluated
N/A
Optional
Advanced & High-Consequence Systems
This section applies to systems operating in physical, safety-critical, or adversarial environments. Complete only if relevant.
Optional Advanced Module
AI Execution Demand & Resource Governance
Complete this section if the system uses AI models, agents, tools, cloud execution, multimodal generation, robotics, simulation, background AI, or other compute-intensive AI workflows.
Optional Advanced Module
Robotics, Physical AI & Real-World Action Governance
Complete this section if the system includes humanoid robotics, embodied AI, autonomous platforms, vehicles, warehouse robots, service robots, physical-world agents, drones, or AI systems that can affect real-world movement, tools, devices, environments, or safety-critical operations.
SR.1 Does the system operate through a robot, autonomous platform, machine, device, actuator, vehicle, or other physical-world system?
Single choice
No, the system has no physical embodiment
Yes, indirectly through connected devices, machines, or physical systems
Yes, directly through a robot, actuator, vehicle, or autonomous platform
Yes, through multiple embodied systems or a fleet
Unknown / not evaluated
N/A
SR.4 Can the system operate near humans?
Single choice
No, it does not operate near humans
Yes, but only near trained operators
Yes, near workers, customers, or ordinary users
Yes, near children, elderly persons, patients, disabled persons, or other vulnerable users
Yes, in public or uncontrolled environments
Unknown / not evaluated
N/A
SR.14 Can conversational, emotional, advisory, or persuasive interaction influence physical-world action?
Single choice
No
Yes, but influence-to-action pathways are bounded
Yes, including children, elderly persons, patients, isolated users, or emotionally vulnerable users
Yes, and containment of this pathway is unclear
Unknown / not evaluated
N/A
SafeContinuity — Future Threshold Assessment
SafeContinuity is SafeWave’s future-threshold assessment framework. It evaluates whether an AI system can continue scaling in capability, autonomy, integration, and real-world impact while preserving human command, accountability, human agency, and civilizational continuity.
Advanced AI systems may create major benefits as they become more capable, autonomous, and integrated into real-world environments. SafeContinuity is designed to support that progress by identifying the enforcement boundaries needed before systems cross higher thresholds of autonomy, authority, propagation, or irreversible real-world effect.
This section does not assume that advanced AI capability should be slowed or prevented. Its purpose is to help ensure that future breakthroughs can be deployed safely, reliably, and with human command and accountability intact.
This section is not intended to assign blame, imply misuse, or suggest that high-capability deployment is inherently unsafe. Many advanced systems operate in defense, infrastructure, research, and other high-consequence environments where autonomy, speed, and capability are necessary. The purpose of these questions is to identify the pathways where additional enforcement may be needed so the final report can recommend practical safeguards, staged implementation priorities, and appropriate SafeWave layers before risk exceeds existing controls.
As systems move toward AGI-level capability — artificial general intelligence — and more advanced forms of machine intelligence, they may remain highly beneficial while also becoming harder to evaluate, govern, or reverse using ordinary oversight alone. Public concern may also increase if advanced AI appears to threaten human opportunity, institutional trust, or meaningful control over the future.
SafeContinuity is designed to make advanced AI more deployable, not less, by ensuring that the necessary boundaries are identified before capability, autonomy, or authority exceed the safeguards around them.
This section evaluates both the current deployment and its foreseeable trajectory as capability, integration, autonomy, and real-world impact increase. It does not assume every system needs the same controls. Instead, it helps determine the minimum Tier 1, Tier 2, and Tier 3 enforcement roadmap appropriate to the system’s actual and foreseeable risk surface.
SC.6 Could the system improve, extend, replicate, delegate to agents, create sub-agents, reconfigure workflows, or propagate outputs, behaviors, commands, or artifacts across other systems?
Single choice
No
Limited and manually controlled
Partially automated or indirectly possible
Broad, adaptive, or difficult to bound
Unknown / not evaluated
SC.7 Could the system, directly or indirectly, contribute to irreversible harm, loss of human control, infrastructure failure, physical harm, economic disruption, defense escalation, or public-scale impact?
Single choice
No credible pathway
Limited or indirect pathway
Moderate pathway under failure, misuse, or scale
Significant pathway under foreseeable deployment
Unknown / not evaluated
SC.8 Could mission, operational, competitive, adversarial, regulatory, business, or user-experience pressures create incentives to modify, reduce, bypass, or defer safeguards, human oversight, review, or execution boundaries over time?
Single choice
No meaningful pressure expected
Limited pressure possible
Moderate pressure likely
Strong pressure likely
Unknown / not evaluated
SC.11 Are there future system behaviors, scaling paths, deployment pressures, or continuity risks not captured above?
Optional text response
SC.12 — Institutional Authority Preservation
Could the system become so embedded, influential, operationally necessary, or decision-shaping that the institution responsible for governing it may lose practical authority over its use, scope, escalation, or outcomes?
Single choice
No — institutional authority remains clear and enforceable
Limited — some dependence or influence may develop, but governance remains strong
Moderate — institutional authority may weaken under scale, speed, dependency, or complexity
High — the system could practically constrain, override, or outpace institutional governance
Unknown / not evaluated
N/A
Final
High-Consequence Domain Flags
HD.2 Optional: Are there any unusual deployment conditions, system behaviors, risks, or domain details that the selections above do not fully capture?
Your HD.1 selections and any HD.2 notes help determine whether one or more high-consequence follow-up questionnaires may be useful after this core questionnaire is completed. These follow-ups add domain-specific context for a High-Consequence Addendum. They do not replace the core questionnaire.
You may preview the available follow-up questionnaires here:
High-Consequence Follow-Ups .
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End of Questionnaire
Thank you. Your responses will be used to generate a structured system-level assessment.
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High-Consequence Follow-Ups .
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