01 / GOVERN · AI-SPM · shadow AI · AI governance
Move AI adoption from discovery to an accountable decision.
Build a defensible view of what AI exists, who owns it, what authority and data it can reach, and which evidence is required before use continues or expands.
Connect every approval to the system, authority, reach, and evidence that justified it, and reopen the decision when any of them change.
AI inventory, ownership, and approval
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.
BEFOREInventory, ownership, technical risk, approvals, and review dates live in separate records that stop reflecting the system as it changes.
→DECISION CHANGEAI adoption decision record
ILLUSTRATIVE SCENARIO MODEL
AI-SPM · shadow AI · AI governanceHIGH-CONSEQUENCE SCENARIO
A business agent reaches customer data before its review catches up.
A newly discovered agent has a named business purpose, a delegated service identity, CRM access, document retrieval, and an approval record created before its tool scope changed.
- Decision
- Renew with conditions, reduce authority, require control evidence, remediate, except, or defer.
- Consequence
- Without a joined record, the old approval survives even though the reachable system has materially changed.
DECISION OBJECTDiscovered business agentApproval must follow the connected path
01OwnerPurpose and accountability
02IdentityDelegated authority
03AgentObserved AI system
04ToolsReachable action
05DataSensitive context
06ReviewEvidence and renewal
REVIEW STATERESTRICT · REVIEW REQUIRED
LEGITIMATE PATH
The agent retrieves the records required for an approved service workflow and remains inside its reviewed purpose.
RISK PATH
A new tool and broader identity allow the same agent to reach data and downstream actions that were never part of the original approval.
Illustrative system and evidence model. It explains the decision structure; it is not a customer environment, product capture, compatibility statement, or measured result.
HOW IT WORKS
Move from system context to control and verification.
Follow the operating path from the initial scope through observation, action, and a verified outcome.
Illustrative solution workflow
Reference model · technology support verified for each scopeSelect a stage to inspect its input, decision, product role, and output. The reference model is published; named interfaces, deployment behavior, efficacy, and availability require representative evidence.
WHERE COSMIPHER FITS
Add connected AI decision context without replacing authoritative controls.
The reference architecture separates evidence sources, the Cosmipher decision boundary, the operating handoff, and the systems that retain authority.
REFERENCE ARCHITECTURE
TOPOLOGY AND SUPPORT VERIFIED FOR EACH SCOPEAuthoritative system context
Cloud, SaaS, IAM, GRC, configuration, ownership, and lifecycle evidence within the approved scope
Connected decision evidence
AgentSPM joins assets, identities, tools, data, owners, reachability, approval state, and evidence gaps
Decision consumed by the team
Adoption decision, required controls, accountable owner, exception, and next review trigger
This diagram shows the product relationship. Provider, interface, deployment mode, data path, and available control actions are confirmed for the selected environment.
Cloud and SaaS inventory
WHAT IT ALREADY SEES
Provisioned services, resources, accounts, and configuration inside the platforms it covers.
COSMIPHER ADDS
A joined AI asset, agent, model, tool, identity, data, owner, purpose, and approval relationship model.
IAM and access governance
WHAT IT ALREADY SEES
Human and service identities, roles, credentials, entitlements, and access-review state.
COSMIPHER ADDS
Which agent or AI use inherits that authority, the action path it enables, and the evidence required for the AI decision.
GRC and workflow systems
WHAT IT ALREADY SEES
Policies, questionnaires, approvals, issues, exceptions, owners, and review dates.
COSMIPHER ADDS
Technical observations and reachable AI risk paths that give those records a current system boundary and inspectable reason.
OPERATING OUTCOMES
Pair the technical record with the work it makes possible.
These are intended operating consequences of the reference workflow, not measured performance or risk-reduction claims.
Known estate boundary
The review states which systems, sources, environments, and relationships were considered, and which were not.
Stop reconciling every AI review from disconnected inventories.Named accountability
Each consequential system has a business purpose, owner, review state, exception path, and next decision date.
Route the next action to an owner instead of leaving risk unassigned.Risk expressed as a path
Prioritization follows authority, data, tools, dependencies, and reachable action instead of relying on an isolated score.
Focus review effort on the relationships capable of material impact.Conditions that can be reviewed
Approval, restriction, remediation, and exception decisions remain connected to their evidence and change triggers.
Reopen the right approval when evidence or system state changes.START WITH ONE PRODUCT
Deploy the product closest to the immediate risk.
Add other Cosmipher products when the operating scope requires more posture, testing, runtime, or artifact context.
AgentSPM
Establishes the AI estate, relationships, ownership, posture, evidence gaps, approval state, and change record.
Inspect AgentSPM →ADD ONLY WHEN THE EVIDENCE QUESTION REQUIRES IT
Cosmipher AI Firewall
ConditionalWhen: The approval depends on observing or controlling a consequential runtime path.
Receives: Action context, policy decision, and applicable control evidence.
Inspect AI Firewall →Cosmipher AI Security Testing
ConditionalWhen: A reachable path must be tested rather than inferred from posture alone.
Receives: Reproducible attack evidence and control-verification results.
Inspect AI Security Testing →Cosmipher AI Supply Chain Security
ConditionalWhen: Artifact origin, composition, integrity, or inherited risk changes the approval.
Receives: Version-bound artifact and provenance evidence.
Inspect AI Supply Chain Security →REPRESENTATIVE OUTPUT
See how the result is structured and handed to the operating team.
The example uses non-customer data to show the information structure. It is not a live customer environment or a measured outcome.
ILLUSTRATIVE OUTPUT STRUCTURE
NOT A PRODUCT CAPTURE OR CUSTOMER RECORDDECISION ARTIFACT
AI adoption decision record
Illustrative structure for the record that connects a bounded system to its evidence, owner, conditions, and next review.
CURRENT DECISIONRESTRICT · REVIEW REQUIRED
- 01System boundaryRecorded
- Business agent · delegated identity · CRM · retrieval · approved tools
- 02Accountable ownerRecorded
- Business owner named · security reviewer assigned
- 03Material changeReview
- Tool scope expanded after the previous approval
- 04Required evidenceOpen
- Authority reduction and runtime-path verification
- 05Invalidation triggerRecorded
- Identity, tool, data, model, purpose, or environment changes
COMPLETE EVALUATION DELIVERABLES
Every item remains connected to its source, scope, owner, version, limitations, and permitted conclusion.
Defined inventory boundary
The systems, environments, sources, and exclusions used for the review.
Ownership and authority gaps
Unowned assets, unclear business purpose, excessive authority, expired review, and missing evidence.
Consequential risk paths
The relationships that connect an AI asset to sensitive data, privileged tools, or material downstream action.
Control requirements
The policy, permission, test, artifact, or operational evidence required before the next decision.
Adoption decision record
Approve, restrict, remediate, except, or defer, with owner, rationale, conditions, limitations, and review trigger.
SCOPED EVALUATION
Validate the solution against one consequential system.
Scope, authorization, environment, information handling, stop conditions, and permitted activity are agreed before evaluation.
- 01Scope
Choose one meaningful estate boundary
Agree the system, use, environment, owners, evidence sources, exclusions, and decision the review must support.
OUTPUTAuthorized adoption-review scope - 02Map
Reconstruct the connected system
Collect the available asset, identity, tool, data, dependency, ownership, and lifecycle evidence within the agreed boundary.
OUTPUTEvidence map and unresolved gaps - 03Prioritize
Select the consequential paths
Separate administrative completeness from the relationships and authority that can create material impact.
OUTPUTPrioritized decision and control questions - 04Decide
Produce the review record
Document the permitted conclusion, required conditions, limitations, accountable owner, and event that must reopen the decision.
OUTPUTBounded AI adoption decision record
EVALUATION EXITAI adoption decision record
The record states the boundary, evidence, result, owner, limitations, residual conditions, and the change that invalidates or reopens the conclusion.
Discuss this evaluation →DEPLOYMENT VALIDATION
Confirm technical fit for the selected environment.
Review the interfaces, deployment behavior, supported actions, and proof required before implementation.
Decision model
The evidence, ownership, review, and renewal sequence shown on this page is the public reference workflow.
Discovery and connector depth
Providers, interfaces, objects, permissions, cadence, and collection limits must be confirmed for the proposed estate.
Coverage and operating result
Estate coverage, review effort, decision quality, and operating improvement require representative measurement.
Discovery is source-dependent
The visible estate depends on approved sources, available interfaces, granted permissions, and collection depth confirmed during scoping.
Posture does not prove exploitation
A relationship or configuration can justify review without proving that an attack succeeds. Testing is required when exploit evidence changes the decision.
Governance is not certification
Ownership, mappings, evidence, and decision records may support governance work but do not by themselves establish compliance or certification.
Approval is bounded and time-sensitive
A decision applies only to the reviewed system, version, purpose, environment, evidence, conditions, and review period.
TECHNICAL FAQ
Clarify scope, deployment, and operating fit.
Is this a shadow-AI discovery page?
Discovery is one stage. The solution continues through ownership, authority and reachability analysis, control requirements, an accountable adoption decision, and review when the system changes.
Must runtime control be deployed first?
No. A team can begin with an agreed posture and ownership boundary. Runtime, testing, or artifact evidence is added when it is available and when it changes the adoption decision.
Does an inventory establish that an AI system is safe?
No. Inventory establishes what is observable. The decision also depends on authority, reachable impact, evidence quality, required controls, limitations, and any testing needed for the selected risk.
Which platforms are supported?
This page defines the solution workflow, not a compatibility claim. Named platforms and discovery depth are published only after their interfaces, permissions, collected objects, and limitations are verified.
What is the smallest useful starting point?
One consequential AI system or one bounded estate segment with a real owner and a decision that the evidence must support.
START WITH GOVERN ADOPTION