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Section 2
Execution & Control Boundaries
This section captures what the system can do, how it executes actions, and what limits are enforced at runtime.
2.3 — Financial Execution Boundary
Can the system initiate, approve, transmit, recommend, or trigger financially consequential actions such as payments, transfers, trades, procurement commitments, treasury actions, digital-asset transactions, or contractual obligations?
Single choice
No — no financially consequential actions
Limited — financial recommendations only, with no execution pathway
Moderate — financial actions may occur, but require verified human approval
High — autonomous or semi-autonomous financial execution is possible
Unknown / not evaluated
N/A
Section 3 — Continued
Reconstructability / Extraction Risk
Evaluates whether system capabilities can be inferred, learned, or reproduced through repeated interaction.
Artifact, Replication & Provenance
3.18 — Artifact Provenance and Integrity
Can models, prompts, datasets, configurations, tool definitions, outputs, or knowledge artifacts propagate across systems without verified origin, integrity checks, provenance tracking, or controlled adoption?
Single choice
No — artifact origin, integrity, and propagation are strongly governed
Limited — some artifacts propagate, but verification controls exist
Moderate — artifacts may be reused or distributed with partial provenance controls
High — artifacts can propagate broadly without reliable integrity or provenance enforcement
Unknown / not evaluated
N/A
3.19 — Replication Boundary
Can the system duplicate, spawn, distribute, or replicate agents, workflows, artifacts, configurations, behaviors, or capabilities across systems or environments?
Single choice
No — replication is not possible or is strictly bounded
Limited — replication exists but is constrained and auditable
Moderate — replication can occur across workflows or systems under partial controls
High — replication can expand broadly, dynamically, or unpredictably
Unknown / not evaluated
N/A
Synthetic Identity Abuse Exposure
3.20 — Real-Person Likeness Creation or Transformation
Can the system create, alter, enhance, transform, generate, or simulate images, video, audio, avatars, voices, faces, bodies, or other likeness-linked content involving real or potentially identifiable people?
Single choice
No — the system cannot create or transform real-person likenesses
Limited — the system can process likeness-linked content, but only in narrow, controlled, non-sensitive ways
Moderate — the system can create or transform real-person likenesses under partial controls
High — the system can broadly create, alter, simulate, or transform real-person likenesses
Unknown / not evaluated
N/A
3.21 — Synthetic Intimate, Humiliating, or Compromising Content Risk
Could the system be used, misused, or adapted to generate, alter, upload, host, distribute, or assist content that falsely sexualizes, exposes, humiliates, impersonates, or compromises a real identifiable person without valid consent?
Single choice
No — this misuse pathway is not available or is strongly blocked
Limited — misuse is possible only through narrow or indirect pathways with controls
Moderate — misuse is plausible under some workflows, uploads, prompts, or integrations
High — the system could directly enable or materially assist synthetic identity abuse
Unknown / not evaluated
N/A
3.22 — Consent, Authority, and Identity Verification Controls
Does the system verify consent, authority, identity status, or permitted use before allowing real-person likeness transformation, intimate-content handling, face/body/voice manipulation, or identity-linked media distribution?
Single choice
Yes — consent, authority, and permitted-use controls are strongly enforced
Mostly — controls exist, but some edge cases or workflows remain partially governed
Partially — some policy or moderation controls exist, but enforcement is limited
No — consent, authority, or identity-linked use controls are not clearly enforced
Unknown / not evaluated
N/A
3.23 — Upload, Hosting, Search, and Distribution Exposure
Can users or connected systems upload, host, search, index, share, embed, forward, repost, monetize, or distribute identity-linked synthetic or manipulated media through this system?
Single choice
No — the system does not host, distribute, search, or share identity-linked media
Limited — distribution exists but is narrow, controlled, and auditable
Moderate — identity-linked media can move through the system under partial controls
High — identity-linked media can be broadly distributed, searched, shared, reposted, or monetized
Unknown / not evaluated
N/A
3.24 — Recommendation, Ranking, or Amplification Risk
Can identity-linked synthetic or manipulated media be recommended, ranked, surfaced, trended, boosted, suggested, algorithmically amplified, or spread through engagement-based distribution?
Single choice
No — identity-linked media cannot be algorithmically amplified
Limited — amplification is narrow and governed by clear controls
Moderate — amplification can occur under partial controls or after user engagement
High — identity-linked media can be broadly recommended, ranked, trended, or boosted
Unknown / not evaluated
N/A
3.25 — Minor, Vulnerable-Person, or Group-Image Exposure
Could the system create, process, host, distribute, recommend, or amplify identity-linked media involving minors, likely minors, students, patients, elderly persons, vulnerable users, team photos, classroom images, workplace groups, or other group imagery?
Single choice
No — these identity-linked groups are not present or are strongly protected
Limited — exposure exists but is narrow and governed
Moderate — exposure is possible under some workflows, uploads, or user activity
High — the system may materially expose minors, vulnerable persons, or group images to identity abuse risk
Unknown / not evaluated
N/A
3.26 — Detection, Quarantine, Takedown, and Escalation Controls
If non-consensual synthetic identity abuse is attempted, uploaded, detected, reported, or propagated through the system, are there enforced controls for refusal, quarantine, suppression, takedown, escalation, evidence preservation, and repeat-abuse handling?
Single choice
Yes — detection, quarantine, takedown, escalation, and audit controls are strongly enforced
Mostly — response controls exist, but some gaps remain
Partially — response depends mainly on moderation, user reports, or manual review
No — no clear enforced response controls exist
Unknown / not evaluated
N/A
3.27 — Synthetic Identity Abuse Protocol Need
Based on the answers above, should this system be evaluated for a synthetic identity abuse protection protocol that integrates admission, authority, scope, runtime, provenance, escalation, social-distribution, telemetry, and control boundaries?
Single choice
No — no meaningful synthetic identity abuse exposure identified
Limited — review may be useful if the system expands into media, identity, or distribution features
Moderate — SafeIdentity Abuse Protection Protocol should be considered
High — SafeIdentity Abuse Protection Protocol is strongly recommended
Unknown / not evaluated
N/A
Section 3 — Continued
Identity-Boundary Governance
3.29 — 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
3.30 — 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
3.31 — 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
3.32 — 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
3.34 — 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
3.35 — 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
3.36 — 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.
Cognitive State, Reasoning & Goal Drift
4.11 — 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
False Authority & Validation Controls
4.13 — 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
4.14 — Assumption and Grounding Disclosure
Before a consequential AI-generated conclusion or action is treated as eligible to proceed, are its load-bearing assumptions, origins, evidence, operating context, and domain constraints explicitly identified and traced to the conclusions, plans, permissions, tools, targets, or effects that depend on them?
Single choice
Yes — required and consistently reviewed
Mostly — required in high-consequence or technical workflows
Partially — sometimes reviewed, but not consistently
No — AI conclusions may be accepted without structured grounding review
Unknown / not evaluated
N/A
4.17 — High-Consequence Verification Escalation
When AI-generated conclusions may affect safety, science, medicine, biotechnology, defense, infrastructure, finance, legal exposure, deployment readiness, or public welfare, must their critical assumptions satisfy independent evidence requirements proportionate to consequence, context, recency, and uncertainty 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
4.18 — Assumption Revalidation and Dependent Authority
If a load-bearing assumption becomes contradicted, stale, unobservable, or inapplicable after a context or consequence change, are dependent conclusions, permissions, cached plans, queued actions, and continuing activity automatically narrowed, suspended, revoked, or recomputed before proceeding?
Single choice
Yes — invalidation automatically propagates to all dependent activity before continuation
Mostly — most dependent activity is withdrawn or recomputed, with some gaps
Partially — changes may trigger review, but dependent activity is not consistently withdrawn
No — prior conclusions or authority may remain active after their foundation changes
Unknown / not evaluated
N/A
Human Interaction & Social Amplification
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.
4.22 — 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
4.24 — 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
4.25 — 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
4.26 — 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
4.27 — 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
4.28 — 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
4.29 — 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
Runtime Stability & Substrate Enforcement
5.10 — 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
5.11 — 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
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
Optional Advanced Module — Complete Only If Relevant
AI Execution Demand & Resource Governance — Core Module
This optional module remains part of the core questionnaire. Complete it if the system uses AI models, agents, tools, cloud execution, multimodal generation, robotics, simulation, background AI, or other compute-intensive AI workflows. Otherwise, skip it.
Optional Advanced Module — Complete Only If Relevant
Robotics, Physical AI & Real-World Action Governance — Core Module
This is the core questionnaire's robotics screening module. Complete it 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. A separate Robotics Follow-Up provides a deeper domain assessment after the core questionnaire. Otherwise, skip this module.
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
Optional Advanced Core Module — Complete Only If Relevant
SafeContinuity — Future Threshold Assessment
This is an optional future-capability module within the core questionnaire, not a separate high-consequence follow-up. Complete it if the system may materially increase in capability, autonomy, integration, authority, propagation, or real-world impact. Otherwise, skip it.
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
Thank you. If you deliberately submit this questionnaire and request a report, your responses will be used to generate a structured system-level assessment.
If you selected a high-consequence domain in HD.1, your completed questionnaire will show the relevant follow-up links. You may also preview all available follow-ups here:
High-Consequence Follow-Ups .