Unknown systems inherit real authority.
An AI service, embedded model, or internal agent can reach identities, data, and tools before ownership and approval records catch up.
Review the adoption decisionCOSMIPHER / CONNECTED AI SECURITY
Secure the systems behind every AI action.
Discover what AI can reach. Exercise how the system can fail. Control consequential interactions and actions. Verify the models, data, dependencies, and evidence behind the decision.
Start with one owned system, one consequential decision, and the smallest useful evidence boundary.
REACHABLE SYSTEM
01
InstructionPurpose and requested task
02
IdentityActor and delegated authority
03
System contextMemory, retrieval, data, and state
04
Tool pathArguments, target, and consequence
DECISION BOUNDARYShould this action proceed?Context + authority + policy + evidence
PROPORTIONATE RESPONSE
ALLOWInside approved purpose
RESTRICTReduce scope or transform
ESCALATERequire accountable review
STOPPrevent the unsafe path
CHOOSE THE NEXT QUESTIONMove directly from the homepage claim to the public record that can answer it.
WHICH DECISION?Adoption, runtime, or releaseHOW DOES IT WORK?Inspect the control planesHOW DO WE BEGIN?Define an evaluation boundaryWHAT IS SUPPORTED?Review the assurance stateTHE OPERATING PROBLEM
AI risk follows what the system can reach.
A prompt or model response is only one part of the path. Business impact emerges when an AI system combines context with identity, data, tools, dependencies, and authority.
A valid action can still be wrong in context.
Identity, purpose, destination, sequence, and consequence determine whether an agent action should complete, not the prompt alone.
Review the runtime decisionSafe-looking output can hide an exploitable path.
A model response does not reveal whether retrieval, memory, tools, dependencies, or delegated agents created a failure elsewhere in the system.
Review the release decisionTHREE OPERATING OUTCOMES
Secure adoption, agent actions, and AI releases.
Start where risk is most urgent. Each solution connects the relevant products, teams, and controls without requiring the entire platform on day one.
01 / GOVERNAI inventory, ownership, and approval
Govern AI Adoption
Govern AI adoption with current evidence, accountable ownership, and risk-based approval.Replace assumption-based adoption decisions with a reviewable record of ownership, reach, risk, required controls, exceptions, and renewal conditions.
- Start with
- One proposed, known, or suspected consequential AI system
- Delivered outcome
- AI adoption decision record
02 / CONTROLRuntime agent action security
Secure Agentic Operations
Authorize every consequential agent action with identity, purpose, context, and policy.Turn an opaque prompt-to-tool sequence into a contextual runtime decision with a reviewable enforcement, exception, and investigation record.
- Start with
- One consequential agent-to-tool or agent-to-system action path
- Delivered outcome
- Runtime decision and replay record
03 / ASSUREAttack, artifact, and release evidence
Assure AI Releases
Release AI systems with version-bound attack, control, and artifact evidence.Replace disconnected scan reports and red-team findings with a version-bound release record showing what was inspected, attacked, changed, replayed, and left unresolved.
- Start with
- One release candidate or high-consequence AI boundary
- Delivered outcome
- Version-bound AI release evidence packet
ONE PLATFORM / FOUR CONTROL PLANES
Four products protecting the same AI system.
Discover the estate, test exploitable paths, control runtime actions, and verify the artifacts entering production while keeping the underlying context connected.
01 / ESTATEEstablish
AgentSPM
Build the observable system boundary across AI assets, owners, identities, tools, data, dependencies, and reachable actions.
- Contributes
- Estate and exposure context
02 / EXPOSUREExercise
Cosmipher AI Security Testing
Test authorized failure paths, preserve the exact trace, and connect remediation to the case that must be replayed.
- Contributes
- Reproducible attack evidence
03 / ENFORCEMENTDecide
Cosmipher AI Firewall
Evaluate identity, instruction, context, tool, target, and policy before a consequential interaction or action completes.
- Contributes
- Contextual control decision
04 / INTEGRITYVerify
Cosmipher AI Supply Chain Security
Examine the models, datasets, artifacts, dependencies, and lineage entering or changing the reviewed system.
- Contributes
- Version-bound trust evidence
PUBLIC ARCHITECTURE BOUNDARYThe relationship shown here is a decision and evidence model. Exact interfaces, collection depth, enforcement points, supported environments, and data exchange are confirmed for the proposed scope.
A CREDIBLE WAY TO BEGIN
Turn one AI boundary into a decision-ready security record.
Select an estate, an agent action path, or an exact release candidate. Define what must be examined, which evidence matters, and what the result is permitted to support.
01Adoption exposureWhich AI systems may proceed, under whose authority, and with which review conditions?→02Action controlShould this consequential agent action proceed, and can the control decision be reconstructed?→03Release evidenceDoes this exact release candidate have sufficient artifact, attack, remediation, and replay evidence to proceed?→
ASSESSMENT BOUNDARYQualification is not authorization.
- 01Public
Share the decision and high-level, non-sensitive context.
- 02Scoped
Name the owner, system boundary, environment, and evidence need.
- 03Agreed
Set access, handling, safeguards, exclusions, and stop conditions in writing.
- 04Authorized
Begin technical work only inside the approved boundary.
A website request, email, meeting, or commercial conversation never grants access or testing authority.
Compare all assessment pathsPUBLIC ASSURANCE RECORD
Know which claims are public, written, service-specific, or not made.
Security review starts from the state of the evidence, not from a badge, framework name, or assumption applied across every offering.
Published
Public methods and boundariesPublic contact boundary, diligence sequence, responsibility model, and current assurance position.
Defined in writing
Assessment handling termsData categories, purpose, channels, participants, restrictions, retention, deletion, location, and third-party involvement are resolved for an approved assessment.
Service evidence required
Exact service review requiredHosting, encryption, access, logging, isolation, availability, and recovery statements must match the exact service under review.
Not claimed
No certification, audit, or public VDP claimCosmipher does not currently present certification, attestation, or independent-audit claims on this website.
The public record includes the diligence sequence, information boundary, responsibility model, current assurance state, and evaluator questions.
Inspect Security & TrustHOW COSMIPHER OPERATES
Keep security findings connected to action.
Cosmipher connects what was observed, what can create impact, which control responded, and what the operating team should do next.
- 01
System before symptom
Treat the model, application, agent, identity, data, tools, and dependencies as one reachable system.
- 02
Decision before alert
Connect each observation to the adoption, runtime, or release decision it is intended to change.
- 03
Boundary before access
Define ownership, scope, authority, safeguards, and stop conditions before technical work begins.
- 04
Evidence before claim
Separate what is public, what is defined in writing, what requires service evidence, and what is not claimed.
START WITH ONE AI SYSTEM
Focus on the risk that matters first.
Identify the owner, reachable impact, current controls, and the product or assessment needed to move forward.