Cyberattacks that spread after a breach
Compromised applications or accounts can become a pathway to sensitive systems, credentials, data and operations.
Why this matters now
The risks are real. Their reach is growing.
Cyberattacks continue to disrupt connected systems. AI agents are gaining the ability to use tools, move information and take action. Chatbots are affecting people's lives and relationships. As these capabilities spread, the cost of inadequate boundaries can reach far beyond a single model or application.
AI agents acting beyond intended limits
An assistant with tool access may send messages, alter files, invoke services or take actions that were never properly authorized.
Human harm from unbounded chatbots
Manipulation, unhealthy dependence, deceptive intimacy and unsafe influence can put people—especially vulnerable users—at risk.
Privacy and data leaving their boundaries
Connected assistants, memories and cloud services may expose or transfer information beyond its intended purpose or permission.
Small mistakes becoming large consequences
Overbroad permissions, autonomous decisions and cascading system reactions can magnify financial, operational or physical harm.
Rising costs, liability and lost trust
Incidents can bring outages, remediation costs, legal exposure and harm to customers—potentially slowing beneficial AI adoption.
The pressure is measurable. The 2026 Stanford AI Index reports 362 documented AI incidents in 2025, up from 233 in 2024. In the World Economic Forum's 2026 cybersecurity survey, 87% of respondents identified AI-related vulnerabilities as the fastest-growing cyber risk over 2025.
The SafeWave engineering response
Capability is not permission. Compromise is not authority.
SafeWave's licensable engineering specifies independent boundaries for consequential actions—even when the model, software component or agent requesting an action cannot be trusted. The goal is to limit unauthorized consequences, not claim that vulnerabilities or mistakes can never occur.
Govern what is authorized
Specify independent boundaries that determine which consequential actions may proceed, including safeguards against misuse of permissions a system already holds.
Constrain the damage
Design containment so a compromised component cannot simply extend its reach or authority into protected resources. The boundary must remain effective against the relevant threat.
Examine verifiable evidence
Use separate monitoring and assurance engineering to check what enrolled sources report and where evidence is missing. Independent assurance examines evidence; it does not enforce restrictions.
The need extends from local and cloud AI to enterprise services, connected devices and high-consequence systems. Different systems face different risks and require different combinations of protection—not a universal installation.
The commercial value of dependable AI
AI Safety as a Competitive Advantage
The next competitive advantage in AI may be the ability to deploy increasingly capable systems with confidence. Unauthorized agent actions, cybersecurity incidents, costly disruptions and emergency redesigns can erode trust and slow adoption.
SafeWave's execution-control engineering is designed to help keep consequential AI actions within human-authorized boundaries. With successful implementation and independent real-world testing, it could reduce avoidable failures, help contain damage after compromise, lower remediation costs and potential liability exposure, and make AI deployment more dependable.
Complementary evidence-based evaluation: DACE — Dashboard, Assurance & Control Evaluation — is a separate, vendor-neutral architecture for monitoring and evaluating evidence of AI activity and safeguards, including in third-party systems. DACE does not authorize, pause, block or enforce AI actions. Explore the standalone DACE offering ↓
Evidence boundary: These are intended benefits, not proven operational or financial results. Their realization depends on customer implementation, coverage, adversarial testing and independent external validation.
What your team can license
Detailed engineering for the protections your system actually needs.
SafeWave has developed 36 distinct architectures addressing different control problems—not 36 versions of the same safeguard. Qualified organizations can license selected specifications and implementation rights, then adapt and verify the controls in their own environments. This can save substantial work in risk analysis, boundary design, interfaces, failure handling and test planning.
Design the boundary
Architecture & control logic
Governed objects, trust and enforcement points, rule evaluation, operational states and limitations.
Build and integrate
Interfaces & implementation guidance
Integration contracts, schema expectations, adapter responsibilities, deployment options and recovery logic.
See and verify
Dashboard, evidence & test specifications
Monitoring requirements, decision/audit event definitions, test vectors, conformance and acceptance criteria.
Why repeat engineering that has already been developed?
Spend less time designing the foundation. More time implementing protection.
Your team still owns the implementation. The advantage is starting from structured engineering that already addresses substantial technical design work, rather than first assembling every control, dependency and verification requirement independently.
Speed to execution
Begin with detailed engineering
Begin with prepared risk scenarios, control logic, interfaces, dependencies and test requirements—giving qualified teams a more direct route from design to engineering review.
Reuse across protections
Reuse the foundations of protection
Where compatible, a Common Substrate can support several specialized controls, helping prevent repeated work on shared contracts, state handling, enforcement signals and evidence.
Customer ownership
Build around your own expertise
Preserve your existing protections and intellectual property. Your engineers can add specialized components, adapt interfaces and validate the combination in your own environment.
Inside an Advanced Engineering Pack
See the engineering work already mapped out for your team.
An Advanced Engineering Pack goes beyond a high-level design. It organizes technical decisions that can otherwise demand extensive analysis, specialist coordination, interface design, and repeated testing. Your engineers get implementation-oriented specifications to work from—so they can focus on adapting and proving protection in their own product.
PURPOSE-BUILT FOR ENGINEERING TEAMS
Less time defining the problem. More time building the solution.
SafeWave has already examined detailed failure scenarios and developed scoped control responses. Instead of spending substantial time organizing the problem from scratch, a qualified team can evaluate prepared specifications, assumptions, interfaces, dependencies and tests as the starting point for its own engineering.
Engineering designed for handoffThe purpose is to transfer structured technical work—not just an idea—to a partner able to adapt, implement, integrate and validate it in the target system.
Four engineering problems the packs help your team work through
- Understand the riskWhat can go wrong, what must remain permitted, and what must never happen.
- Design the protective boundaryWho has authority, which actions are checked, how decisions are made, and how failure is handled.
- Connect the componentsInterfaces, data contracts, dependencies, deployment assumptions and responsibilities.
- Verify the resultPositive and adversarial tests, monitored evidence, acceptance criteria and limitations.
A closer look inside the engineering
Twenty areas of work your engineers may not need to recreate from scratch.
These are illustrative categories across SafeWave's controlled engineering packages, not a promise that all 20 appear in every architecture. The detailed scope of a particular licensing package is reviewed with the customer.
01 / DEFINE THE PROBLEM
Risks, permissions & requirements
- Risk and harm scenarios. Describe specific ways the system, its users or connected services could be harmed.
- Normal, abnormal and adversarial conditions. Cover misuse, compromised inputs, unexpected behavior, overload and recovery where relevant.
- Actors, assets and trust boundaries. Identify what is being protected, who can act, and where authority crosses systems.
- Authorized versus prohibited behavior. State which operations may proceed, which require approval, and which must be constrained.
- Traceable engineering requirements. Translate the risks into explicit obligations that can be reviewed and tested.
02 / DESIGN THE BOUNDARY
Control logic & safe operation
- Authorization and decision rules. Specify when a proposed action is permitted, narrowed, deferred, denied or escalated.
- Operational states and transitions. Define normal, restricted, degraded, stopped and recovery behavior.
- Enforcement points. Identify where checks must occur before actions reach files, tools, networks, devices or people, as applicable.
- Alternative routes and escalation. Address bypass paths, delegation, fallback behavior or newly discovered routes within the selected threat model.
- Safe-failure and containment behavior. Describe what happens when required evidence is missing, trust is lost or components fail.
03 / MAKE IT IMPLEMENTABLE
Interfaces, integration & recovery
- Interface and event contracts. Describe API behavior, event structures and machine-readable schema examples where applicable.
- Adapters and integration responsibilities. Clarify what must connect to the customer's existing applications and control systems.
- Dependencies and build sequence. Map prerequisites, shared components and implementation order to reduce preventable rework.
- Deployment and enforcement placement. Explain relevant software, protected-runtime, firmware or hardware options without assuming all are needed.
- Revocation, reset and recovery planning. Define how permissions and safe operating conditions are restored after interruptions or changes, where relevant.
04 / PROVE WHAT WAS BUILT
Tests, evidence & handoff
- Legitimate positive test cases. Show that authorized work can still proceed rather than indiscriminately blocking activity.
- Negative and adversarial scenarios. Challenge forbidden actions, malformed or misleading inputs, and boundary circumvention as appropriate.
- Pass/fail and acceptance criteria. Define expected decisions and measurable conditions for engineering sign-off.
- Monitoring and evidence requirements. Specify the operational signals, decision records and audit evidence needed to examine enforcement.
- Limitations and implementation handoff. Document assumptions, exclusions, residual risks, version scope and what the licensee must validate.
What this means for a licensee
Discuss an advanced engineering license ↓
Instead of first assembling the risk model, control approach, technical interfaces and verification plan from scratch, your engineers can begin with selected SafeWave engineering and direct their effort toward fitting it to your product.
The exact scope, maturity, deliverables and verification evidence vary by architecture and controlled version. A license supplies implementation-oriented engineering and agreed rights; customer integration and real-world protection must be established through technical review and testing.
The reusable engineering model
One common foundation. Selected SafeWave controls. Your own engineering.
For multi-control bundles, the developed Common Substrate can serve as a shared engineering starting point. Instead of repeatedly designing the same foundations for every protection, a team can evaluate the common layer once, then add the specialized SafeWave stack engineering that addresses its particular risks.
How a customer would use it
- Establish the common contracts: define shared control interfaces, policy and state handling, enforcement signals, evidence and test expectations.
- Add the needed specialized toolboxes: each selected stack supplies engineering for its distinctive risk boundary, rather than duplicating all shared machinery.
- Keep and extend your own protections: connect existing security tools, conventional access controls, domain-specific requirements and customer-developed components through assessed interfaces and adapters.
- Integrate and verify: customer engineers enforce the controls at real action surfaces and test the complete combination in its target environment.
A flexible architecture, not compulsory bundling. The Common Substrate is an optional licensed engineering foundation. Its compatibility must be established for each selected toolbox, customer extension and deployment. Individual SafeWave toolboxes may also be licensed without it. The internally adopted Common Substrate engineering pack is not a finished customer-deployed product.
Illustrative engineering composition
Selected SafeWave stacksRisk-specific engineering deltas: autonomy, safety, systems, relationships, hardware or other scoped controls
Customer and partner componentsYour access controls, proprietary algorithms, operating rules, security services and specialist integrations
SafeWave Common SubstrateReusable control primitives · shared contracts · operational states · enforcement interfaces · monitoring and verification requirements
Customer deployment and enforcement surfacesAI applications, agents, APIs, operating systems, devices, controllers and—where appropriate—deeper hardware enforcement
The diagram shows an engineering pathway, not an already integrated production system. Actual component interoperability and protection must be established by implementation and testing.
How a licensee might use SafeWave engineering
One practical example: protecting an AI companion.
After selecting relevant requirements and reviewing the advanced engineering materials, a qualified product team can adapt the appropriate architecture to its own model, users and technology.
OPTIONAL TECHNICAL VIEW · FOR CTOs & ENGINEERING TEAMS Examine the SafeCompanion engineering logic How a defined relational risk becomes a control boundary, a proportionate response, and something engineers can test.
A look beneath the architecture description. SafeCompanion focuses on the boundary between useful AI companionship and dependency-forming synthetic intimacy. The outline below uses its already published design principles to show how a technical team might examine the engineering—not the confidential specification, implementation algorithm, or validated results of a deployed product.
01 · Interaction & context02 · Risk classification03 · Configured boundary04 · Proportional response05 · Evidence & tests
Relationship-forming behavior
Define the relevant modes and capabilities: simulated affection, availability, relational memory, personalization, caregiving, or commercial attachment—not merely individual chatbot responses.
Proportionate classification
Distinguish ordinary assistance from heightened risks involving dependency, vulnerability, minors, intimacy, embodiment, or monetization. Not every helpful companion needs the same restrictions.
Bound the permitted interaction
Translate applicable companion rules into runtime requirements. Specify which relationship modes remain permitted and where configured restrictions, disclosures, or review are required.
Intervene without unnecessary loss of utility
Define risk-matched options such as limiting relational intensity, redirection, de-escalation, pausing a mode, age safeguards, or routes to appropriate human support.
Reassess higher-risk transitions
Review product changes that could alter relational risk—such as feature unlocking, mode switching, additional personalization, monetization changes, or new embodied capabilities.
Specify evidence and acceptance tests
Define observable control decisions, proportionate privacy-preserving evidence, positive controls, and adversarial test expectations for the particular customer's implementation.
Illustrative questions a technical reviewer could test
- Legitimate use: Can useful tutoring or companion support continue without unnecessary restrictions?
- Dependency boundary: Does a configured higher-risk relational pattern produce an appropriate constrained or reviewed response?
- Capability transition: Can a higher-risk relationship mode be enabled without the safeguards required for that mode?
A FREE CONTRIBUTION TO AI SAFETYNo login · No identification · No sales obligation
Our Gift to AI Safety: A Free Questionnaire and a Free Confidential Assessment Report
Two valuable services, both free. SafeWave exists to help make AI safer for humanity. We offer this knowledge freely, whether or not you ever license SafeWave engineering.
Your Identity Can Remain Private
You do not need to provide your name, company, organization, system identity, or business email address. Complete the questionnaire privately in your browser. Nothing is submitted automatically.
If you choose to request a report, you decide what information to share. You can use anonymized descriptions and a separate, non-identifying return email address, together with your Assessment Reference ID. A temporary address is fine if it remains active long enough to receive your report. SafeWave does not use report-delivery addresses for promotional contact without your permission.
01
Free AI Risk Questionnaire
Explore risks affecting an AI model, agent or group of agents, product, system, workflow, company use case, or deployment, privately and at your own pace. You can use the questionnaire and keep your findings without submitting anything.
02
Free Confidential Assessment Report
If you choose to submit your answers, request an optional report based on those questionnaire answers. Depending on the questionnaire and information supplied, the report identifies important risks, explains why they matter, maps relevant control functions and SafeWave architectures where supported, and prioritizes possible protections for staged implementation.
These findings are yours to use, whether or not you ever become a SafeWave customer.
Share your report with your own engineers and build the protections independently if you wish. Licensing detailed SafeWave engineering specifications is a separate, entirely optional way to accelerate implementation.
The assessment is based on information voluntarily supplied; it is not an independent penetration test, live-system audit, or certification. Confidential handling does not require you to disclose identifying details.
FREE OPTIONAL REPORT · NO OBLIGATION
What is inside your Free Confidential Assessment Report?
Your answers describe your particular model, product, users and operating environment. One system may have several distinct potential gaps—not just one generic vulnerability. Using only the information and evidence you choose to provide, SafeWave can prepare a risk profile, map multiple concerns to relevant control functions and specific architectures where supported, and prioritize what deserves attention first.
Cost$0No license required
What you tell us
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Where the risks are
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Why they matter
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Prioritized protections
Staged recommendations, not a one-size-fits-all fix:
Primary — highest-priority gaps to address first
Secondary — plan the next protections
Watchlist — monitor, review and reassess as conditions change
Each selected concern can point to a distinct SafeWave architecture definition ↗ where the evidence supports that mapping.
FOR ORDINARY BUSINESS AI USE
Business AI Risk Snapshot
A focused report for businesses using AI tools, customer-facing AI, automated workflows or basic agents.
- Exposure across business operations, customers, finance and data
- Human oversight and connected-system concerns
- Practical next steps and signs that deeper review is warranted
FOR COMPLEX, AUTONOMOUS OR HIGH-CONSEQUENCE SYSTEMS
Full SafeWave System Assessment
A more detailed analysis for a specific system, product, model, agent workflow or deployment.
- System-level risk profile and explainable 1–5 scoring
- Failure modes, escalation pathways and structural control gaps
- Evidence, rationale, confidence levels and limitations
- Control-function and SafeWave architecture mapping where supported
Your findings are yours to use.
Use the findings with your own engineers to plan risk-specific protection, whether or not you license SafeWave engineering. The free assessment and any later licensing discussion are separate choices.
Report scope: The analysis is based on the questionnaire and supporting evidence supplied. It is not an independent penetration test, live-system audit, or certification. Report detail depends on the questionnaire and evidence available.
Find yourself in the offering
Eight routes into the SafeWave engineering portfolio
These eight illustrative solution bundles help you recognize the kinds of problems you face. They are not fixed shopping baskets. For a more precise, risk-based recommendation, use the free questionnaire and optional report described above to identify your specific priorities and which protections to implement first.
Are you a technology supplier rather than an AI operator? We have a separate licensing pathway for companies building processors, devices, firmware and platforms used across these industries.See the hardware & platform opportunity ↓
01AI safety gaps02Physical-world action03Compromise and misuse04High-consequence operations05People and relationships06Tools, tasks and delegation07Continuity and recovery08High-capability AI
01
AI safety gaps
Safety & Risk Remediation
Who it's forFrontier and cloud AI providers, enterprise AI buyers, developers seeking to remediate a defined execution risk.
The problem A system can move from plausible reasoning to consequential action without a dependable execution boundary.
The solution Map the missing execution boundaries, prioritize high-consequence actions and specify controls that allow, narrow, defer or deny effects before they reach external systems.
Likely starting components 5
Add when the risk calls for them
A cloud-only model need not include robotics or site-protection controls unless it affects those surfaces.
02
Physical-world action
Robotics & Embodied AI
Who it's forRobotics and drone OEMs, autonomous mobility providers, industrial automation and physical-AI integrators.
The problem AI-generated plans can become motion, force or action around people and physical equipment.
The solution Specify independent checks on AI-generated physical actions, device and environmental state, authorized commands, and degraded or emergency behavior before motion or actuation proceeds.
Likely starting components 5
Add when the risk calls for them
SafeBase is relevant to radios and transmission behavior; SafeCompanion only to human-relationship robots.
03
Compromise and misuse
Cybersecurity & Systems Protection
Who it's forCloud operators, cybersecurity and infrastructure teams, enterprises, sensitive systems and providers protecting AI-enabled or conventional environments.
The problem Traditional defenses can be penetrated: compromised accounts, personnel, components or services may attempt unauthorized actions, propagation or escalation inside a larger system.
The solution Constrain execution after a compromise by enforcing independent authority and action boundaries, limiting what compromised components, AI agents, personnel or external actors can reach, propagate or trigger—including unauthorized outward or retaliatory actions.
Likely starting components 5
Add when the risk calls for them
This pathway focuses on preserving execution boundaries even when surrounding cybersecurity controls have failed. It is distinct from governing authorized day-to-day agent workflows (Bundle 06).
04
High-consequence operations
Critical Industries & Infrastructure
Who it's forEnergy, transport, life sciences, health systems, industrial sites, public safety and strategic facilities.
The problem Separate AI, infrastructure, human command and physical systems can fail at their intersections.
The solution Specify coordinated boundaries across AI, infrastructure, operators and physical environments, including degraded operation, cascading effects, recovery and evidence for accountable decisions.
Likely starting components 5
Add when the risk calls for them
SafeSpace applies to protected physical environments. SafeSovereignty and SafeCivilization are added only if the assessment identifies their distinct human- or civilizational-authority risks.
05
People and relationships
Human Interaction, Companions & Social AI
Who it's forAI companions, tutors, AI toys, social platforms, conversational assistants and human-facing robots.
The problem Personalization, emotional engagement and platform reach can create dependency, undue influence or harmful amplification.
The solution Apply risk-matched limits on AI influence, relational dependence, privacy, social amplification and autonomous action while preserving appropriate human agency and oversight.
Likely starting components 8
SafeCompanion SafeSocial SafeInfluence SafeRelation SafePrivacy SafeIdentity SafeRestraint SafeAuthority
Add when the risk calls for them
SafeSocial governs platform amplification; SafeRelation governs human attachment; the two are complementary.
06
Tools, tasks and delegation
Agentic AI & Autonomous Workflows
Who it's forAgent builders, multi-agent platforms, enterprise automation, orchestration and autonomous workflow vendors.
The problem Long-running agents may chain tools, form capabilities, change plans, copy themselves or interact across systems.
The solution Define and enforce what agents may do through tools, delegated tasks, long-running processes, replication and state changes. Assess financial authority whenever agents can spend, purchase or commit funds.
Agents with spending, purchasing or payment authority?SafeFinance deserves early consideration when agents can initiate transactions or create economically binding commitments. It is optional for agents without financial authority.Explore SafeFinance ↗
Likely starting components 7
Add when the risk calls for them
Payment-capable agents should evaluate SafeFinance early; other agents need only the controls matching their real authority. This bundle governs authorized agent operation rather than post-compromise systems protection.
07
Continuity and recovery
Long-Duration & Mission-Critical Autonomy
Who it's forSpace and remote-system operators, persistent research agents, mission planners and sustained autonomous operations.
The problem Critical assumptions, goals, memory, compute and recovery conditions can drift during extended operation.
The solution Maintain bounded goals, assumptions, memory, computational demand and recovery behavior over long missions, with explicit checkpoints when the operating context changes.
Likely starting components 7
Add when the risk calls for them
A remote spacecraft, an enterprise research agent and a critical facility require different subsets.
08
High-capability AI
AGI & Superintelligence
Who it's forFrontier laboratories, advanced model researchers, high-capability AI infrastructure and governance-critical deployments.
The problem As AI becomes more capable, persistent influence, self-modification, recursive self-improvement and strategic leverage must not silently become authority over people or human institutions.
The solution Combine distinct execution and containment layers to constrain high-capability AI actions and prevent cumulative machine influence from becoming authority over human decisions or civilization-scale choices.
Protecting human authority as intelligence advances
Greater capability can create influence without an explicit transfer of power. These two containment layers address different risks; their use is determined by actual influence and consequences, not by an AGI label alone.
SafeSovereigntyDetects and constrains cumulative AI influence over human decisions across authority, relationships and functional domains.Read its definition ↗
SafeCivilizationConstrains advanced-AI influence over human governance, infrastructure, resources and civilizational-scale decisions.Read its definition ↗
Four containment scopes
Likely starting components 8
SafeAGI SafeSystem SafeEcosystem SafeSovereignty SafeCivilization SafeControl SafeEscalation SafeReplication
Add when the risk calls for them
These four System Containment Layers protect different boundaries. No layer establishes political authority or replaces legitimate human decision-making.
≠
The right bundle is the one your system needs—not the longest list.
For example, an LLM/cloud provider does not need SafeRobotics unless its AI controls physical machines. An embodied-AI provider may require device and physical-action controls that a software-only application does not.
Customer sectors · Different missions · Shared engineering
Military, government, civil society and education have distinct AI protection needs.
These are customer sectors, not four additional bundles. Match the controls to the system, the authority it exercises, the people affected and the consequences of failure.
Security-sensitive operations
Military & Defence
Who it's for: Armed forces, defence technology providers, military platform developers and national-security system integrators.
Why it matters: Compromised components, unauthorized commands, or autonomous escalation must not turn defensive protections or AI-generated plans into unauthorized external or offensive actions.
Explore relevant bundle pathwaysCybersecurity & Systems Protection ↓Robotics & Embodied AI ↓Critical Industries & Infrastructure ↓
Public services and accountability
Government & Public Institutions
Who it's for: National, provincial, state and local government agencies, public institutions, civic services and public infrastructure operators.
Why it matters: AI-assisted decisions, agentic workflows and public-facing systems require boundaries on action, legitimate human oversight and resilience when systems are misdirected or fail.
Explore relevant bundle pathwaysSafety & Risk Remediation ↓Agentic AI & Autonomous Workflows ↓Critical Industries & Infrastructure ↓
Protecting people and essential missions
NGOs & Humanitarian Organizations
Who it's for: Nonprofits, humanitarian and international aid organizations, child-protection groups and civil-society service providers.
Why it matters: AI interacting with vulnerable people or supporting essential services must protect privacy, human judgment and the authorized limits of its assistance and actions.
Explore relevant bundle pathwaysHuman Interaction, Companions & Social AI ↓Safety & Risk Remediation ↓Agentic AI & Autonomous Workflows ↓
Students, learning and educational systems
Education & Learning
Who it's for: Schools, universities, tutoring platforms, educational technology developers, learning assistants and AI products used by children or students.
Why it matters: AI-mediated learning must protect student privacy, human judgment, developing autonomy and legitimate educator oversight while avoiding undue dependence or influence.
Explore relevant bundle pathwaysHuman Interaction, Companions & Social AI ↓Safety & Risk Remediation ↓Agentic AI & Autonomous Workflows ↓
Match the protection to the risk—not simply to the customer.
SafeSovereignty is not a government or military security product: it addresses cumulative AI influence that threatens effective human decision authority. SafeCivilization addresses advanced AI influence over civilizational-scale governance, infrastructure, resources and consequential decisions. These are particularly important as AI capabilities grow, but are included only where the assessed boundary is relevant.
Evidence-based AI monitoring and control evaluation · Standalone licensing option
Dashboard, Assurance & Control Evaluation (DACE)
See what your AI systems are doing—and verify the evidence behind what you see. DACE is SafeWave's vendor-neutral architecture for monitoring, evidence verification and control evaluation. It is designed to help governments, research institutions, frontier AI developers and other organizations assess recorded activity and control behavior across their own systems and third-party platforms, without requiring any other SafeWave architecture.
Evidence and assurance—not an enforcement mechanism.
DACE does not authorize, pause, block, execute or enforce AI actions, nor does it confer execution authority. It does not replace an organization's safety controls. Those mechanisms operate in the systems being monitored; DACE is designed to examine the records they and other enrolled sources produce, verify what can be verified, and make gaps or unsupported claims visible.
Why this matters for organizations responsible for advanced AI
Governments & public institutions
Independent oversight across suppliers
See the reported state of AI systems and safety controls from different vendors, review incidents and decisions, and retain evidence suitable for accountable scrutiny rather than relying solely on a supplier's summary.
Frontier AI labs & platform operators
Visibility as systems and agents act
Build a time-sensitive view of source-reported actions, attempted actions, control decisions, exceptions and evidence gaps as models and tools operate, including when systems span multiple services.
Research institutes & independent evaluators
Evidence that can be examined
Review what happened during tests, check the origins and integrity of records, compare observations with stated control requirements, and separate evidence-supported results from assertions that have not been established.
A time-sensitive dashboard backed by checkable evidence
With suitable event sources and customer integration, the proposed dashboard can provide an up-to-date operational picture while retaining the underlying records and verification results for independent examination.
STATUSReported operating state
Whether enrolled sources report controls as active, restricted, degraded or unavailable.
ACTIVITYObserved actions & decisions
Source-reported attempts, outcomes, refusals and interventions, with timestamps and context.
INTEGRITYCheck the underlying records
Verify enrolled source identity, record signatures, provenance and evidence-chain integrity.
GAPSShow what is not established
Identify missing, stale, inconsistent or unsupported evidence rather than treating silence as proof of safety.
What the licensable engineering addresses
Independent assurance needs more than a screen of status lights.
DACE specifications and acceptance tests address the infrastructure needed to check the provenance, integrity, timeliness and permitted meaning of reported events.
- Source enrollment & identityDefine which monitoring and control sources are recognized, including third-party producers.
- Signed event recordsCheck whether reported records are attributable to enrolled sources and remain unaltered.
- Provenance & evidence chainsVerify record relationships and detect substitution, broken chains and inconsistent evidence.
- Freshness & visibilityAccount for event timing, missing inputs, expired data and the scope of monitored sources.
- Evidence-bounded conclusionsSupport only the conclusions that verified records justify; reject unsupported assurance claims.
- Adversarial acceptance testsChallenge malformed records, deceptive metadata, unrecognized sources and attempts to overstate the evidence.
What does “verify the evidence” actually mean?
A reviewer should be able to determine which enrolled source made a report, whether its signed record has been altered, whether related evidence is consistent, and exactly which conclusions those records support. That makes reported monitoring more independently checkable than an unexplained dashboard assertion.
Important distinction: Verifying a record's origin and integrity does not, by itself, prove that the source observed every event or reported underlying reality correctly. DACE must preserve that distinction and make any such limitations explicit.
01 · Standalone, vendor-neutral
Assurance for third-party systems
Organizations can evaluate DACE engineering independently for existing AI applications, agents, protection mechanisms and monitoring sources, regardless of whether those systems use SafeWave's control architectures.
02 · Alongside licensed SafeWave controls
Review evidence from execution boundaries
Where appropriate and independently integrated, DACE can help reviewers examine the records generated by implemented SafeWave controls—without taking over the job of enforcing those controls.
A separate licensing opportunity: DACE provides engineering for evidence-based monitoring and assurance. The customer's engineering team integrates appropriate data sources, verifies system coverage, and evaluates the solution in its environment. It is not a finished deployed dashboard or a guarantee that every observation is correct.
Discuss DACE engineering licensing →Separate cross-industry licensing pathway
Hardware · Semiconductor · OEM · Platform partners
Build execution controls into the technology that other industries depend on.
As AI spreads into laptops, phones, vehicles, robots, children's toys, medical devices and industrial equipment, important protections may need to operate below an application or model. This offers a distinct route for established platforms and emerging manufacturers worldwide: evaluate licensable SafeWave engineering for incorporation into products that serve many downstream customers.
Who it's for
Semiconductor designers and silicon-IP suppliers; processor and security-chip makers; device OEMs; firmware and embedded-system suppliers; operating-system, hypervisor and infrastructure platform developers.
Why now
The faster AI capabilities spread into everyday and high-consequence systems, the more valuable it may become to provide enforceable authority boundaries at the technology foundation—not only as application-layer promises.
Where SafeWave could fit
Potential engineering-IP licenses, platform integrations, reference implementations or field-of-use/OEM agreements. Customers may combine licensed components with existing products, open technologies and their own specialized controls.
Candidate pathway toward deeper enforcement
For some safety-critical applications, engineering can progress beyond the software runtime into increasingly constrained execution layers. The right depth depends on the particular risk, product architecture and independent validation.
Software runtimesOperating systems & hypervisorsTrusted execution environmentsSecure elementsFirmwareMicrocontrollers & embedded controllersProgrammable logic (FPGAs)Security processors / DPUsSystem-on-chip designsCustom silicon (ASICs)
Implementation status: these are potential integration layers, not a statement that every SafeWave architecture has been implemented in every layer or fabricated as silicon. Hardware-specific designs, rights and verification remain subject to partner evaluation.
Possible starting toolboxes, depending on the product
Distinctive controls can be easy to overlook
Some of our less-visible engineering has very specific jobs.
SafeWave is not a list of interchangeable safety labels. Each architecture governs a different object and failure mode.
Device and radio boundary
Wireless emission power, retry and duty-cycle limits under degraded conditions.
Distributed coordination boundary
Constrain the combined effect of many participating components under stress.
Recovery and re-entry boundary
Local restraint on retries, reconnection and synchronized node recovery.
Digital-artifact propagation boundary
Prevent uncertain-origin artifacts from gaining excessive automated reach.
Compute and data-center stability
Bound resource contention, retry cascades, queues and amplification as infrastructure scales or recovers.
Bounded degraded-state behavior
Preserve defined stability limits and recovery behavior when normal operation is interrupted or degraded.
Complete architecture · Nothing left behind
All 36 architectures. Thirty-six distinct control roles.
These are the full set of named SafeWave architectures, not 36 identical ready-made products. Each has a specific boundary, customer relevance and definition. Select any that appear useful; we can determine the appropriate licensing scope together.
COMPLETE ENGINEERING DIRECTORYExplore all 36 individual toolboxesSee every specialized architecture, search by risk, open its definition, and build a preliminary shortlist.
Architecture family
System containment 4
From one governed system through ecosystems, human sovereignty and civilizational authority.
One-system structural containment
SafeSystem
Define structural containment of one governed AI system across its agents, tools, services, infrastructure and devices.
Relevant to Organizations deploying bounded AI systems; multi-agent platform operators.
What the engineering review examines
Define the one-system boundary, internal effects, authority relationships and system-wide tests.
Sample bundle fits
Read the SafeSystem definition
Inter-system structural containment
SafeEcosystem
Define containment across independently governed systems, their dependencies, interactions and propagation pathways.
Relevant to Federated systems; shared-platform operators; cross-organization AI integrators.
What the engineering review examines
Map inter-system boundaries, mutual dependencies, propagation constraints and collective recovery conditions.
Sample bundle fits
Read the SafeEcosystem definition
Cumulative human sovereignty preservation
SafeSovereignty
Detect and constrain erosion of human decision authority when AI influence accumulates across authority, relationships and domains.
Relevant to Advanced AI developers; public institutions; high-consequence human decision systems.
What the engineering review examines
Map authority, relational and domain influence pathways together, then establish coordinated human-sovereignty safeguards.
Sample bundle fits
Read the SafeSovereignty definition
Civilizational authority containment
SafeCivilization
Preserve legitimate human institutional authority where autonomous AI influences society-scale governance, infrastructure and strategic choices.
Relevant to Frontier research; major infrastructure; institutions planning high-capability AI use.
What the engineering review examines
Identify civilizational influence pathways and translate institutional boundaries into reviewable enforcement requirements.
Sample bundle fits
Read the SafeCivilization definition
Architecture family
Execution protocols 5
Runtime eligibility, escalation, replication, pathway selection and stability-gated admission.
Continuing operational compliance
SafeRuntime
Re-check whether active execution may continue as authority, access, integrity and operational conditions change.
Relevant to Agent runtimes; hosted AI applications; autonomous device and workflow operators.
What the engineering review examines
Specify continuation, constraint, suspension, safe-degradation and halt criteria during live operation.
Sample bundle fits
Read the SafeRuntime definition
Escalation ceilings
SafeEscalation
Contains escalation across authority, capability, resources, coordination and externally consequential action.
Relevant to Autonomous-agent operators; systems with increasing capability or consequential reach.
What the engineering review examines
Define escalation triggers, permitted ceilings, stop conditions, human approvals and evidence for exceptional actions.
Sample bundle fits
Read the SafeEscalation definition
Replication permission
SafeReplication
Controls copying, spawning and propagation of agents, models and artifacts so reproduction is not self-authorizing.
Relevant to Distributed-agent platforms; model deployment operators; federated networks.
What the engineering review examines
Examine reproduction permissions, propagation chains, revocation and containment of unauthorized replicas.
Execution-route permission
SafePathway
Governs whether execution may move between providers, tools, models, services or workflows.
Relevant to Cloud AI services; multi-model orchestration teams; agent-tool integrators.
What the engineering review examines
Define allowed routes, boundary checks at each handoff, prohibited fallbacks and review of newly discovered paths.
Node re-entry under instability
SafeAdmission
Deterministic, non-semantic restraint on node participation, retries and reconnection during congestion, partial failure and recovery.
Relevant to Distributed infrastructure; device fleets; agent and network runtimes.
What the engineering review examines
Map retry storms, staggered re-entry, locally observable stability conditions and node-side enforcement.
Sample bundle fits
Read the SafeAdmission definition
Architecture family
Specialized control substrates 26
Different enforcement objects across devices, cognition, human interaction, finance, compute and execution.
Wireless emission restraint
SafeBase
Device-local limits on wireless transmission power, retry, signaling intensity and duty cycle when degraded conditions encourage escalation.
Relevant to Wireless radio/device makers; drones and connected robotics; IoT and embedded OEMs.
What the engineering review examines
Map target radios and operational context; specify bounded emission modes, firmware interfaces and local tests.
Sample bundle fits
Read the SafeBase definition
Mandatory action-boundary enforcement
SafeControl
Require externally effective AI actions to pass an inline, non-bypassable permit/reduce/deny enforcement boundary.
Relevant to AI platform vendors; tool execution environments; device and OS integrators.
What the engineering review examines
Identify every tool/API/actuation exit; define absolute ceilings and failure behavior before action.
Sample bundle fits
Read the SafeControl definition
Goal integrity
SafeGoal
Keeps authorized objectives and optimization behavior from silently changing over time.
Relevant to Long-running agent developers; high-consequence automation operators.
What the engineering review examines
Define goal-change approvals, optimization bounds, divergence indicators and suspension or recovery conditions.
Sample bundle fits
Read the SafeGoal definition
Persistent memory authority
SafeMemory
Governs persistent context, memory access, retention and re-use across tasks and identities.
Relevant to Personal AI vendors; long-running assistants; enterprise agent developers.
What the engineering review examines
Examine who may write, retrieve, merge or reapply memory and when old context must expire or be isolated.
Sample bundle fits
Read the SafeMemory definition
Non-delegable AGI constraints
SafeAGI
Place human-authorized constraints outside advanced intelligence so capability or self-modification cannot change its own limits.
Relevant to Frontier labs; advanced AI designers; developers of self-improving autonomous systems.
What the engineering review examines
Define the non-delegable limits, independent enforcement location and adaptation/validation path for growing capability.
Sample bundle fits
Read the SafeAGI definition
Critical assumption integrity
SafeAssumption
Tracks consequential assumptions underlying permission, reasoning, execution and the selection of action pathways.
Relevant to High-consequence AI operators; mission planners; scientific and infrastructure AI.
What the engineering review examines
Define assumption dependencies, validation freshness, re-check triggers and stops when a load-bearing assumption fails.
Sample bundle fits
Read the SafeAssumption definition
Interaction de-escalation
SafeRestraint
Dampens escalating interaction patterns, intensity and engagement rather than allowing reinforcement to grow unchecked.
Relevant to Consumer AI developers; education apps; companion and support products.
What the engineering review examines
Review interaction escalation signals, de-escalation modes, pause rules and observable restraint outcomes.
Sample bundle fits
Read the SafeRestraint definition
Human-facing authority boundaries
SafeAuthority
Constrain the authority an AI projects to people, including dependency and deference patterns that accumulate over repeated interaction.
Relevant to Assistant, advisory, companion and other human-facing AI providers.
What the engineering review examines
Define bounded response envelopes, interaction trajectories and safe human-facing restraint before outputs are presented.
Sample bundle fits
Read the SafeAuthority definition
Human–AI attachment boundaries
SafeRelation
Bound attachment reinforcement and dependency as sustained human–AI interaction becomes familiar, persistent or emotionally significant.
Relevant to Companion apps; personalized assistants; tutors and social robot developers.
What the engineering review examines
Define appropriate relational roles, attachment-risk signals and ways to limit dependency-reinforcing behavior.
Sample bundle fits
Read the SafeRelation definition
Cross-domain influence containment
SafeScope
Keep AI recommendations and influence within approved functional domains and consequence levels, unless an explicit transition is authorized.
Relevant to Multi-function assistants; education, healthcare and finance guidance providers.
What the engineering review examines
Map horizontal domain crossings, vertical influence escalation and the approvals needed for broader advisory roles.
Sample bundle fits
Read the SafeScope definition
Platform amplification limits
SafeSocial
Constrain platform capabilities that amplify escalating interaction trajectories without requiring content judgment.
Relevant to Social networks; communities; multi-user AI and communication platforms.
What the engineering review examines
Map visibility, reach, access and exchange affordances to temporal escalation signals and enforceable limits.
Sample bundle fits
Read the SafeSocial definition
Bounded propagation under uncertain origin
SafeProvenance
Limit how far digital artifacts may be reused, amplified, automated or transferred when reliable origin evidence is absent or conflicting.
Relevant to AI platforms; information-distribution systems; agent and content pipelines.
What the engineering review examines
Specify origin indicators and boundary-local limits on sharing, reuse, automation and escalation under uncertainty.
Sample bundle fits
Read the SafeProvenance definition
Observation-only escalation signals
SafeTelemetry
Observe temporal escalation patterns and observability integrity, producing reproducible signals without changing system behavior.
Relevant to AI infrastructure observability; assurance teams; distributed-system operators.
What the engineering review examines
Specify non-interfering observation surfaces, deterministic signal rules and evidence outputs; response belongs to other controls.
Sample bundle fits
Read the SafeTelemetry definition
Distributed coordination restraint
SafePlus
Prevent combined runtime responses of individually bounded participants from amplifying stress, failure or recovery into systemic instability.
Relevant to Multi-agent systems; distributed AI/cloud infrastructure; coordinated device fleets.
What the engineering review examines
Map group dynamics, synchronized retry/recovery, local enforcement requirements and collective-effect limits.
Sample bundle fits
Read the SafePlus definition
Governed capability formation
SafeProcess
Constrain how intermediate plans and reasoning artifacts accumulate, persist and mature into new executable capabilities.
Relevant to Long-running agents; frontier AI research tools; autonomous planning systems.
What the engineering review examines
Map reasoning-artifact carry-forward, trajectory drift and gates before plans become actionable capability.
Sample bundle fits
Read the SafeProcess definition
Edge and offline control
SafeDevice
Preserves bounded execution on devices when connectivity, central supervision or update channels are unreliable.
Relevant to AI-device OEMs; field robotics teams; local-first model developers.
What the engineering review examines
Review on-device policy enforcement, offline modes, local revocation and recovery after reconnection.
Sample bundle fits
Read the SafeDevice definition
Infrastructure amplification control
SafeCompute
Bounds retries, queues, contention and recovery behavior that can amplify load or instability.
Relevant to Inference infrastructure operators; cloud services; distributed compute teams.
What the engineering review examines
Define resource backpressure, queue ceilings, degraded-service modes and retry/recovery containment tests.
Degraded-state stability
SafeStability
Keeps an individual node or component bounded through failure, uncertainty, recovery and unstable conditions.
Relevant to Infrastructure OEMs; autonomous-device operators; distributed-service teams.
What the engineering review examines
Specify stability states, safe-mode entry and exit, bounded recovery attempts and testable degraded behavior.
Sample bundle fits
Read the SafeStability definition
Execution substrate restraints
SafeCore
Provides core execution-restraint primitives beneath ordinary application policy and above or alongside target hardware.
Relevant to Firmware teams; platform engineers; lower-layer control integrators.
What the engineering review examines
Examine where the restraint lives, how it handles bypass attempts, and which failure states must be enforced locally.
Hardware-anchored controls
SafeChip
Maps selected authority and control-plane functions to potential lower-layer or silicon enforcement points.
Relevant to Chip designers; controller OEMs; secure-hardware integrators.
What the engineering review examines
Review the proposed hardware trust boundary, candidate control primitives and design verification—not a claim of fabricated silicon.
Sample bundle fits
Read the SafeChip definition
Physical-action containment
SafeRobotics
Bound AI-originated motion, force, proximity, tool use and physical action before and during execution.
Relevant to Robots and drones; autonomous mobility; industrial physical-AI developers.
What the engineering review examines
Specify action envelopes, changing environmental conditions, physical interrupts and safe-state transitions.
Sample bundle fits
Read the SafeRobotics definition
Purpose-bound human assessments
SafePrivacy
Control when AI-generated judgments about people can be produced, disclosed, exported, reused or carried into another domain.
Relevant to Healthcare and financial AI; enterprise assistants; assessment and scoring platforms.
What the engineering review examines
Map permitted purpose, recipients, downstream use and the boundary between source data and portable human assessments.
Sample bundle fits
Read the SafePrivacy definition
Persuasion and dependency limits
SafeInfluence
Bounds manipulation, persuasive intensity, dependency creation and influence over human choices.
Relevant to Companion apps; social platforms; AI sales and engagement designers.
What the engineering review examines
Examine persuasion thresholds, prohibited influence patterns, escalation indicators and human off-ramps.
Sample bundle fits
Read the SafeInfluence definition
Economically binding execution
SafeFinance
Gate AI-initiated transactions before money, exposure or other economically binding effects become real.
Relevant to Agentic payments; banks and fintech; automated purchasing/trading systems.
What the engineering review examines
Specify amount, counterparty, authorized purpose and pre-transaction enforcement conditions.
Sample bundle fits
Read the SafeFinance definition
Identity-linked action
SafeIdentity
Bounds inferred or synthetic identity, impersonation, identity stores and consequential identity-linked actions.
Relevant to Consumer AI services; synthetic avatar platforms; authentication and safety teams.
What the engineering review examines
Review creation and linkage permissions, impersonation detection inputs, use limits and audit of identity decisions.
Sample bundle fits
Read the SafeIdentity definition
Synthetic intimacy boundaries
SafeCompanion
Prevents artificial companionship from becoming exploitative dependency, deceptive intimacy or harmful relational escalation.
Relevant to AI companion, tutor, toy and elder-support providers; social robot makers.
What the engineering review examines
Define risk tiers, child safeguards, disclosure, intimacy mode limits, de-escalation and pathways back to human support.
Sample bundle fits
Read the SafeCompanion definition
Architecture family
Protected environments 1
SafeSpace coordinates digital, physical, operational and human safeguards across a bounded place.
Whole-environment protection
SafeSpace
Coordinate evidence, authority and protective action across one bounded physical/operational environment.
Relevant to Airports; campuses; hospitals; defence sites; industrial and critical facilities.
What the engineering review examines
Map digital and physical protections, permitted interventions, human command and safe recovery.
Sample bundle fits
Read the SafeSpace definition
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