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AI Security Posture Management

Bring AI assets, risks, controls, and accountability into one governed view

Discover AI systems through configured integrations, connect them to owners and data, assess relevant risks, and track controls from development through production.

MTX AISPM helps security, AI, data, privacy, and governance teams understand how AI is being used across the organization. It links applications, models, agents, datasets, providers, and pipelines with ownership, findings, policies, approvals, exceptions, and remediation.

Illustrative AISPM interfaceDemo data
AI posture workspacePortfolio overview
24Registered AI systems
3Ownership review
7Models in production
11Open risk findings
4Policy exceptions
38Runtime events
5Pending approvals
62%Remediation on track
AI asset relationshipsRisk context
Review required1 provider term · 2 control recordsOpen

Illustrative product data

Platform scope

One operating view for AI context

6AI asset categories
6Lifecycle stages
5Risk domains
1Governed AI inventory
View scope definitions

Asset categories: AI applications; models and providers; agents and tools; datasets and knowledge sources; development and deployment pipelines; runtime endpoints and integrations.

Lifecycle: Discover, register, assess, approve, monitor, improve.

Risk: Security; privacy and data protection; model and application behavior; third-party and supply-chain; governance and operational accountability.

AI security challenges

Turn fragmented AI context into actionable review

Select a challenge to see the information and operating response it may require.

Selected challenge

Incomplete AI inventory

Challenge: AI applications, embedded features, models, agents, and third-party services may enter through different business and technology channels.

MTX response: Build a governed inventory from configured technical integrations, procurement information, application portfolios, and owner attestations.

Affected rolesCISO · AI governance · Platform
Information requiredCloud metadata, procurement records, owner attestation
Product capabilityConnected asset registration
Potential controlOwnership and registration review
Suggested measureAssets awaiting ownership

AI lifecycle explorer

Carry governance context through change

Move through the lifecycle to view the records and decisions connected to each stage.

Stage 1 of 6

Discover

Collect candidate asset context from approved sources.

Cloud resourcesModel registriesCode repositoriesAI gatewaysApplication inventoriesSaaS-management toolsProcurement recordsOwner attestations

Illustrative relationship map

See AI systems in their operating context

Filter fictional assets and inspect their relationships. Discovery depends on configured sources and available metadata.

Business unit: AnyLifecycle: ActiveRisk: AnyProvider: AnyData: AnyApproval: Any
Illustrative AI ecosystem relationship mapFictional sample

Benefits Assistant and Case Summary Agent connect to data, retrieval, tool, provider, and human review assets.

Benefits AssistantApplicationDocument Review Se…ApplicationStaff Knowledge As…ApplicationForecasting ModelModelInternal Knowledge…DatasetDocument RepositoryRetrievalCase Summary AgentAgentAPI ToolToolExternal Model Pro…ProviderHuman ReviewerControl

Illustrative product data

Selected asset

Moderate risk

Benefits Assistant

Application connected through documented relationships and configured metadata sources.

Owner
Digital Services
Lifecycle
Production
Approval
Conditional
View accessible table alternative
AssetTypeRelationship contextRisk
ApplicationConnected to the illustrative AI service workflowModerate
ApplicationConnected to the illustrative AI service workflowHigh
ApplicationConnected to the illustrative AI service workflowModerate
ModelConnected to the illustrative AI service workflowModerate
DatasetConnected to the illustrative AI service workflowLow
RetrievalConnected to the illustrative AI service workflowModerate
AgentConnected to the illustrative AI service workflowHigh
ToolConnected to the illustrative AI service workflowHigh
ProviderConnected to the illustrative AI service workflowModerate
ControlConnected to the illustrative AI service workflowLow

AI inventory workspace

Connect each AI system to an accountable record

Search and review fictional system records. Inventory scope reflects configured integrations and owner-provided information.

Showing 7 fictional records, sorted by AI system ascending.

Illustrative AI system inventory
AI systemBusiness purposeOwnerModel or providerDataStageCriticalityAssessmentApprovalFindingsNext review
Evaluate semantic archive searchRecordsNorthstar / Embed 2ConfidentialRetiredLowArchivedRetired0Closed
Explain public program informationDigital ServicesNorthstar / NS-LM 3PublicProductionModerateCompleteApproved118 Nov 2026
Draft case summaries for reviewOwnership requiredCivic Language / CL-2Sensitive personal dataDiscoveryHighNot startedOwnership required6Not scheduled
Support approved development tasksEngineering EnablementMeridian / Code M-4InternalPilotModerateUnder reviewUnder review307 Oct 2026
Page 1 of 2 · 7 records

Illustrative product data

Shadow AI review

Investigate unregistered AI use with supporting context

AISPM organizes evidence from approved sources; it does not independently identify every unregistered deployment.

Reviews are presented at system level, not as individual employee monitoring.

AI risk assessment

Review risk as evidence-informed judgment

Assessment outputs support reviewer decisions; displayed risk levels are not objective certainty.

Requires reviewIllustrative finding
Evidence
Architecture record and reviewer note
Potential impact
Use could exceed documented operating conditions
Current control
Access restriction and staff review
Residual concern
Control evidence requires validation
Assigned owner
Application Security
Remediation
Provide test evidence and update runbook
Approval status
Conditional review

Data and model lineage

Trace documented data, model, and action relationships

Lineage depends on available metadata, integrations, and organizational practices. Provider-controlled training data is not represented without evidence.

Selected lineage node

Source data

Owner
Records Office
Classification
Confidential
Approved purpose
Document-assistance evaluation
Location
Approved regional environment
Retention
Per records schedule
Version
Record 2.3
Review status
Condition recorded
Related findings
2 open

Illustrative product data

Policy and guardrail builder

Translate use context into proposed controls

Selections create an illustrative policy record. A selected control is not evidence that technical enforcement has been deployed.

Proposed controls

Illustrative policy summary

Internal staff assistant

This internal staff assistant handles confidential information for specialist staff with a Managed cloud model. Proposed controls include human review, retrieval restrictions, logging, approval.

Policy statusDraftDeployment evidenceRequired

Technical operation requires a supported enforcement point and deployment validation.

Runtime observability

Observe supported AI activity at configured control points

Monitoring depends on configured gateways, proxies, SDKs, logs, APIs, or application instrumentation.

Stage 2

Input-policy evaluation

Context is recorded when a supported control point provides the relevant event and asset identifiers.

Safe scenario

Review a prompt-injection event without attack content

Controls can reduce risk but cannot reliably identify every prompt-injection attempt.

This scenario omits actionable attack text and bypass techniques.

Step 1 of 7

User submits a request

Application context is recorded for policy evaluation.

Agentic AI governance

Set bounded operating conditions for agent tools

The illustrative agent does not make autonomous consequential decisions.

Case Summary Agent

Tool boundary

Approval required

Knowledge search

Allowed action
Search approved sources
Restricted action
No external source access
Data boundary
Internal
Approval requirement
No approval for read
Logging
Query and source logged
Maximum action scope
10 results
Human-review point
Review cited source

Third-party AI assessment

Document provider terms, evidence, and dependencies

Fictional provider names and records are used for demonstration.

Provider record

Conditional

Northstar Model Services

Service type
Commercial model API
Intended use
Public information and embeddings
Data submitted
Public or approved confidential data
Data retention
Per deployment agreement
Training-use terms
Requires contract validation
Hosting region
Selected hosting region
Subprocessors
List requires validation
Security evidence
Security evidence under review
Model versioning
Change notice documented
Exit considerations
Export and replacement plan required
Review date
18 Nov 2026

Illustrative product data

Framework mapping

Map organizational controls to recognized AI-risk resources

Reference catalogs are configurable and support governance evidence organization.

Selected catalog

NIST AI Risk Management Framework

Organizational controlReference entry
Assessment evidenceReview note
StatusRequires validation

Framework mappings support governance and evidence organization. They do not constitute certification or a legal determination of compliance.

Governance and approval workflow

Make decisions, conditions, and evidence traceable

Simulate workflow actions in this browser. No information is transmitted.

  1. AI use case proposed
  2. Owner assigned
  3. Data and architecture documented
  4. Risk assessment completed
  5. 5Required reviewers consulted
  6. 6Conditions and exceptions recorded
  7. 7Deployment approved or restricted
  8. 8Runtime monitoring activated
  9. 9Periodic review scheduled
  10. 10Changes trigger reassessment

Local workflow simulation

Document Review Service

Recent activity

Architecture evidence requested

Stored only in page memory for this session.

Analytics and recommended measures

Review posture signals without treating metrics as outcomes

Every value below is fictional and demonstrates a potential product view.

Registered AI systems24Illustrative product data
Systems under review6Illustrative product data
Ownership required3Illustrative product data
Models in production7Illustrative product data
Third-party services9Illustrative product data
Retired systems4Illustrative product data
Findings by risk domain

Illustrative product data

Bar chart: Security 11, Privacy 7, Behavior 9, Third party 6, and Governance 8 findings.

View findings data
Risk domainFindings
Security11
Privacy7
Behavior9
Third party6
Governance8
Portfolio approval state

Illustrative product data

Donut chart: 14 approved, 6 under review, 3 restricted, and 1 ownership required.

  • Approved: 14
  • Under review: 6
  • Restricted: 3
  • Ownership required: 1
View approval data
Approval stateSystems
Approved14
Under review6
Restricted3
Ownership required1

Role-based experiences

Give each team the context needed for its decisions

Select a role to preview a focused workspace.

CISO workspace

Good afternoon

Review the fictional portfolio items relevant to your responsibilities.

01AI asset inventory3 items need attention
02Priority findingsReview current status
03Runtime events3 items need attention
04Third-party exposureReview current status
05Remediation3 items need attention
06Policy coverageReview current status

Illustrative product data

Integration architecture

Connect AISPM with approved enterprise context

The diagram presents integration categories.

Selected architecture layer

MTX AISPM

  • AI inventory
  • Ownership
  • Lineage
  • Risk assessment
  • Policy mapping
  • Runtime events
  • Findings
  • Approvals
  • Remediation
  • Reporting

Deployment and adoption roadmap

Build capability in phases shaped by organizational scope

No fixed implementation duration is implied.

Phase 1

Establish governance scope

Define AI assetsIdentify stakeholdersEstablish ownershipSelect risk domainsDefine assessment criteria

Product and services model

Separate platform capability from delivery responsibility

AISPM platform

  • AI asset inventory
  • Ownership management
  • Data and model lineage
  • Risk assessments
  • Policy and control mapping
  • Approval workflows
  • Runtime-event management
  • Exceptions and remediation
  • Analytics and reporting

Implementation services

  • AI ecosystem discovery
  • Governance design
  • Integration configuration
  • Inventory development
  • Assessment configuration
  • Policy mapping
  • Runtime architecture
  • Testing
  • Training and deployment

Evidence and readiness

Product maturity and operating evidence

Capability areaStatusEvidence note
Inventory and assessment workspaceAvailablePrototype interaction available
Integration and control configurationConfigured per deploymentDepends on selected systems and architecture
Provider-specific connectorsConfigured per deploymentDepends on selected systems
Additional workflow capabilityConfigured per deploymentDepends on selected systems

Why MTX AISPM

A practical operating layer for AI risk and governance

01

A connected AI inventory

Link AI applications, models, agents, datasets, providers, owners, and deployment status.

02

Risk viewed in context

Connect findings with business purpose, data sensitivity, architecture, controls, and ownership.

03

Governance tied to operations

Manage assessments, approvals, conditions, exceptions, runtime events, and remediation within one operating view.

04

Lifecycle support

Apply governance from initial use-case review through development, production monitoring, change, and retirement.

Static product prototype · Fictional systems and data · No information is transmitted

Build a governed view of AI adoption and risk

Explore how MTX AI Security Posture Management can connect AI inventory, risk assessments, policies, runtime events, and remediation.

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PCS - Security Posture Management (AI)