vanta compliance features

Vanta’s compliance features center on automating evidence collection and continuous control monitoring across many security and privacy frameworks, plus wrapping that in workflow, AI, and Trust Center capabilities.¹²

Core compliance automation

Vanta runs 1,200+ automated tests on an hourly basis to continuously check controls across your infrastructure, identity, endpoint, and other systems, shifting you from point‑in‑time prep to ongoing compliance. These tests pull evidence via 300–375+ integrations (cloud, HRIS, SSO, ticketing, code repos, vulnerability scanners) and centralize it for audit readiness.³⁴⁵¹

Vanta can automate up to roughly 80–90% of work for common frameworks by mapping controls once and reusing them across 30–35+ frameworks, reducing duplicated effort when you add SOC 2, ISO 27001, HIPAA, GDPR, PCI, and others.⁶⁷⁸¹

Frameworks, policies, and documents

Vanta provides pre‑built controls and policy/doc templates aligned to 20+ frameworks, including SOC 2, ISO 27001, HIPAA, and GDPR. You get framework‑specific policy libraries, auto‑generated compliance docs, and system description templates that accelerate scoping and documentation.^9107116

Controls are cross‑mapped so a single implemented control can satisfy requirements in multiple standards (e.g., SOC 2 and HIPAA), which helps you layer frameworks without rebuilding your program.¹⁰¹⁶

Audit prep and GRC workflows

Vanta centralizes audit prep with automated evidence collection, issue tracking, and an in‑app experience for working directly with auditors. GRC features include task assignment and notifications, risk management, personnel/access management, automated access reviews, and automated third‑party security questionnaires.²¹⁹³⁶

You can use integrations with Jira and other task systems for two‑way ticket sync, incident tracking, and remediation workflows tied to non‑compliant controls.¹²¹³¹

Trust Center and questionnaire automation

The Trust Center lets you expose real‑time control status, reports, and documentation externally to customers and prospects as a living trust portal. Questionnaire automation features allow you to respond to customer security questionnaires more efficiently, leveraging your existing artifacts and data.^1415123

Combined with Salesforce and other CRM integrations, you can connect security posture and approvals directly to customer lifecycle stages.¹⁵

AI and agentic capabilities

Vanta AI layers on guided workflows for policy drafting, control remediation, and insights about your compliance posture. Recent releases include an AI “agent” positioned to run or heavily assist with routine compliance workflows, reportedly saving compliance teams significant weekly time.¹⁶²³

If you share your current stack (GCP/K8s, auth, HRIS, ticketing) and target frameworks (e.g., SOC 2 + HIPAA for an agentic SaaS), a follow‑up can map exactly which Vanta features and integrations you’d rely on and where you might still need custom processes. ^17181920

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Related

Compare Vanta features with other compliance tools Which security frameworks does Vanta support How does Vanta handle continuous monitoring Vanta integrations list for cloud and identity tools How Vanta automates audit evidence collecti

Vanta focuses on automated evidence collection, continuous control monitoring, and multi‑framework mapping, similar to peers like Secureframe, Drata, and Hyperproof, but with its own mix of Trust Center, AI, and deep integration coverage.^2122232425

Vanta vs other tools

Dimension	Vanta	Secureframe	Drata	Hyperproof
Core focus	Compliance automation + Trust Center + AI assistant	Compliance automation for 35+ frameworks with AI features	Compliance automation, strong on SOC 2 and continuous monitoring	GRC + compliance + integrated risk management with strong dashboards
Evidence collection	Automated via 300+ integrations, mapped to controls and requests2426	Automated evidence collection across controls and integrations2721	Automated evidence collection from cloud, VCS, IdP, HRIS etc.2228	“Hypersyncs” automated evidence pulls across systems2329
Continuous monitoring	1,200+ automated tests, hourly checks on controls and assets2425	Continuous monitoring of tech stack and controls2721	Near real‑time monitoring of security controls2228	Continuous control status and real‑time dashboards233029
Framework coverage	30+ frameworks (SOC 2, ISO 27001, HIPAA, GDPR, PCI, etc.)3132	35+ global frameworks (SOC 2, ISO, NIS2, GDPR, etc.)2133	Major security frameworks, especially SOC 2 and ISO2234	100+ frameworks including SOC 2, NIST, DORA, FedRAMP, HIPAA, PCI35
AI capabilities	AI agent for compliance workflows and insights2536	AI to enforce standards and automate tasks3721	Generative AI for documentation and predictive risk38	AI to auto‑map controls, validate evidence, answer questionnaires3035
Trust / questionnaires	Built‑in Trust Center, questionnaire automation2539	Vendor risk and questionnaire support via integrations2140	Questionnaire support and auditor collaboration2228	Trust center and questionnaire automation features3035

For a multi‑agent SaaS, the main choice usually hinges on depth of risk management (Hyperproof), breadth of frameworks (Hyperproof/Secureframe), or go‑to‑market friendly Trust Center + automation (Vanta/Drata).²⁸³⁰²¹

Supported security frameworks in Vanta

Vanta supports a broad catalog including SOC 2, ISO 27001, HIPAA, PCI DSS, GDPR, and many others, with more than 30 frameworks and regulations available. It also offers “advanced” frameworks such as NIST CSF, CIS, and sector‑specific add‑ons that can be layered on top of a baseline like SOC 2.³¹³²

Controls are cross‑mapped so implementing a control once can satisfy overlapping requirements across SOC 2, ISO, HIPAA, and GDPR, cutting incremental work as you add frameworks.²⁴⁴¹⁴²

Continuous monitoring in Vanta

Vanta runs continuous automated checks (on the order of 1,200+ tests) across infrastructure, identity, endpoints, ticketing, and code repos, typically on an hourly schedule. These checks verify configurations (e.g., MFA enabled, S3 encryption, CIS hardening), asset status, user access, and vulnerability signals, flagging failed tests as issues to remediate.^25264324

You can tune which checks apply by framework and scope, and route failures into task systems such as Jira for remediation workflows.⁴⁴⁴⁵²⁴

Vanta integrations: cloud and identity

Vanta integrates with major cloud providers including AWS, GCP, Azure, and services like Heroku, plus container and CI platforms used in modern SaaS. It also connects to identity providers such as Okta, Google Workspace, Azure AD, Rippling and others to pull user, group, and SSO configuration data.²⁶⁴⁵⁴⁴

Additional integrations cover HRIS (e.g., BambooHR), ticketing (Jira, Linear), vulnerability scanners, code repos (GitHub, GitLab), and MDM/endpoint tools, which together form the evidence backbone.⁴⁵²⁶⁴⁴

How Vanta automates audit evidence

Vanta maps each control to specific evidence sources and automatically collects screenshots, config states, logs, and reports from integrated systems on a recurring basis. For example, it will continually pull AWS config, GitHub repo settings, IdP user lists, and vulnerability scan results and attach them to the relevant controls and auditor‑facing requests.^28242526

During audit cycles, auditors can be granted access or receive structured exports so they see already‑collected evidence tied to each control test, which compresses audit fieldwork and reduces ad‑hoc evidence chases.³⁰⁴³²⁴

If you share your target frameworks and stack (e.g., GCP + K8s + Okta + Rippling + Jira), a next step can be a concrete integration/control matrix for Vanta vs 1–2 alternatives and a prompt template you can reuse in vendor selection docs. ^{46474849505152}

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Show side-by-side feature comparison of Vanta and Drata

Which frameworks do Drata and Secureframe support besides SOC 2 Examples of continuous monitoring checks Vanta performs List common integrations for evidence collection across tools How do Vanta and Hyperproof automate user access reviews

Vanta, Drata, Secureframe, and Hyperproof all offer automated evidence collection and continuous monitoring, but they diverge in framework breadth, GRC depth, and how they implement access reviews and integrations.^53545556

Vanta vs Drata features

Area	Vanta	Drata
Core positioning	Compliance automation + Trust Center + multi‑framework library5758	Compliance automation platform with strong SOC 2 and ISO focus5559
Evidence collection	100–300+ integrations; pulls configs, access data, scan results into mapped controls606153	120+ connectors; collects AWS configs, Okta logins, device and HR data as evidence556253
Continuous monitoring	~1,200+ automated tests; hourly checks across infra, IdP, endpoint, code, tickets575861	Near real‑time monitoring of connected systems and mapped controls5559
Trust / sharing	Built‑in Trust Center and questionnaire automation5863	Trust Center‑style sharing and auditor collaboration features5559
Access reviews	Dedicated Access Reviews module, automated campaigns, reviewer workflows646566	User access review workflows linked to identity integrations (Okta, Azure AD, etc.)5562
GRC / risk	Lightweight risk and issue management tied to controls5867	Risk register and control mapping with more classic GRC elements5559

Frameworks beyond SOC 2

Drata supports SOC 2 plus ISO 27001, HIPAA, PCI DSS, GDPR, and additional security/privacy frameworks such as ISO 27701 and NIST‑aligned programs. Secureframe covers 35+ global frameworks, including ISO 27001, ISO 27018, HIPAA, HITRUST, PCI DSS, GDPR, CCPA, NIST, and regional mandates like NIS2.^{68555962697071}

Both vendors position SOC 2 as an entry framework and then let you layer additional standards on the same control set, similar to Vanta’s cross‑mapping.⁵⁵⁶⁹⁷²

Examples of Vanta continuous checks

Vanta’s automated tests commonly include checks such as:

• Cloud: S3 buckets encrypted, security groups restricted, IAM policies scoped, GuardDuty/Inspector enabled.⁶⁰⁵⁷⁶¹
• Identity: MFA enforced for Okta/Google Workspace, no shared super‑admin accounts, timely de‑provisioning of offboarded users.⁶⁵⁵⁷⁶⁰
• Endpoint: Devices enrolled in MDM, disk encryption enabled, OS up to date and not jailbroken/rooted.⁷³⁵⁷⁶¹
• SDLC/tickets: GitHub branch protections and reviews, Jira tickets for incidents and changes linked to controls.⁵⁷⁶⁷⁵³

Failed checks surface as issues and can be routed into Jira or similar tools for remediation.⁷⁴⁶⁰⁵⁷

Common integrations used for evidence

Across Vanta, Drata, Secureframe, and Hyperproof, the common backbone integrations for evidence collection are:

• Cloud: AWS, Azure, GCP (plus variants like AWS GovCloud and Heroku).^54536068
• Identity & productivity: Okta, Entra ID/Azure AD, Google Workspace, Microsoft 365.^53605468
• HRIS & devices: Workday, BambooHR, Rippling, Gusto, Jamf, Kandji, Intune.^75546853
• Dev & tickets: GitHub, GitLab, Bitbucket, Jira, Linear, ServiceNow, Slack.^60547553

These connectors let each platform continuously pull user lists, configs, logs, and scan results to keep controls “evidence‑backed” without manual exports.⁵⁴⁷⁵⁵³

Access review automation: Vanta vs Hyperproof

Vanta’s Access Reviews product consolidates account access data from integrated systems (Okta, AWS, apps) into a central hub, assigns system reviewers, runs scheduled review campaigns, and tracks approvals/denials and remediation tasks. It can automatically pull user lists from connected apps, send notifications and reminders, flag orphaned or offboarded accounts, and maintain an audit trail of who approved which access and when.^6476667365

Hyperproof’s User Access Reviews module imports a people directory from HRIS/IdP via “Hypersyncs,” pulls per‑app user lists, then assigns Reviewer and Sysadmin roles to run review campaigns with email/task notifications. Reviewers attest to each user’s access, Sysadmins implement changes in source systems and then attest in Hyperproof, with real‑time status, reporting, and evidence linked to access‑control requirements.^77787980

If you want, the next step can be a control‑level matrix (access control, change management, logging, etc.) showing how each vendor automates evidence and work for a SOC 2 + ISO 27001 baseline and where you’d still need custom glue for a multi‑agent SaaS stack. ^818283848586

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BREAK down in 5 level deep detail the feature, functions, standards, compliance, regulations an AI platform would need to to have to be 1. competitive 2. 10x disruptive in this space with an AGENTIC PLATFORM delivering an enterprise compliance solution and dramaticly drop the price to deliver a complete solution for SBB to large enterprise across finance fintech and healthcare medical devices, pharma industry verticals

A competitive agentic compliance platform needs deep, automated coverage of today’s frameworks and workflows; a 10x‑disruptive one needs autonomous, cross‑regulatory “compliance ops” that reason over regulations, systems, and business context in real time at dramatically lower marginal cost.⁸⁷⁸⁸⁸⁹

Below is a 5‑level decomposition you can use as an architecture checklist.

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1. Market & product surface

1.1 Core product categories

1.1.1 Compliance automation (today’s baseline)

• SOC 2, ISO 27001, HIPAA, GDPR, PCI DSS, NIST CSF, etc., with multi‑framework mappings.⁸⁷
• Evidence collection, continuous controls monitoring, auditor collaboration, Trust Center.⁹⁰⁹¹

1.1.2 GRC / risk platform

• Enterprise risk registry, issues, control library, policy management.⁹²⁸⁸
• Third‑party/vendor risk, questionnaires, DPAs, DPIAs, AI risk modules (NIST AI RMF, ISO/IEC 42001).⁹³⁹⁴⁸⁷

1.1.3 Vertical packs (finance/healthcare/medical devices/pharma)

• Fintech: GLBA, SOX‑adjacent ITGCs, PCI DSS 4.0, PSD2, DORA, AML/KYC support.⁸⁹⁸⁷
• Healthcare & PHI: HIPAA/HITECH, CMS rules, state privacy laws.⁹⁵
• Medical devices & pharma: FDA SaMD guidance, EU MDR/IVDR, EU AI Act high‑risk AI, BS 30440, GMLP.⁹⁶⁹⁷⁹⁸

1.1.4 AI‑specific governance

• EU AI Act, US/EU/JP emerging AI laws, ISO/IEC 42001, NIST AI RMF, sector AI guidance.⁹⁹⁹⁴⁹⁶
• AI model inventory, data lineage, evaluation and monitoring, bias and safety controls.⁹³⁸⁹

1.1.5 Go‑to‑market & pricing

• Tiered SKUs (S&B, mid‑market, enterprise) with per‑framework or per‑agent pricing.¹⁰⁰⁸⁷
• Disruption angle: near‑flat pricing per org with usage‑based compute; default bundles of 5–10 frameworks for SBB.

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2. Framework, standard & regulation coverage

2.1 Security & privacy baselines

2.1.1 Core security frameworks

• SOC 2 (CC1–CC9), ISO 27001/2, CIS, NIST CSF, NIST 800‑53/171, PCI DSS 4.0.⁹⁴⁸⁷
• Mappings to regional mandates: DORA, NIS2, CCPA/CPRA, LGPD, PDPA variants.⁸⁹⁸⁷

2.1.2 Privacy & data protection

• GDPR and derivatives (RoPA, DPIA, DPA templates, data subject rights).⁹⁹⁸⁷
• Cross‑border transfer regimes (SCCs, DPF), consent and purpose‑limitation policies.¹⁰¹⁹⁹

2.1.3 Industry‑specific regulations

• Finance/fintech: PCI DSS 4.0, GLBA Safeguards, FFIEC IT, PSD2, DORA, AML/KYC guidance.⁸⁷⁸⁹
• Healthcare: HIPAA/HITECH, CMS, ONC rules; PHI data handling and logging patterns.⁹⁵
• Medical devices & pharma: FDA SaMD, post‑market surveillance, EU MDR, GMLP.⁹⁷⁹⁸⁹⁶

2.1.4 AI governance and safety

• EU AI Act classification and obligations, especially for high‑risk AI (e.g., medical).⁹⁶⁹⁹
• ISO/IEC 42001 management system for AI; NIST AI RMF for risk.⁹⁴⁸⁷
• Model lifecycle governance: data sourcing, training, validation, deployment, change management.⁹⁶⁹³

2.1.5 Cross‑framework mapping layer (your differentiator)

• Central meta‑control ontology that maps each control to multiple external requirements (e.g., “MFA on critical systems” → SOC 2, ISO 27001, HIPAA, PCI, DORA).⁸⁸⁸⁹
• Agents that automatically reconcile new or updated regulations (PCI 4.0, NIS2, new AI acts) into this graph.⁸⁸⁸⁹

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3. Functional capability stack

3.1 Evidence, monitoring, and workflow

3.1.1 Integrations and telemetry

• Baseline: AWS, Azure, GCP; Okta/Entra/Google Workspace; HRIS; MDM; CI/CD; ticketing; SIEM.¹⁰²⁸⁷
• 10x: standardized “compliance connector SDK” plus agents that infer controls from logs/configs even for unknown tools.⁸⁸⁸⁹

3.1.2 Continuous control monitoring

• Today: automated checks for encryption, MFA, patch status, access de‑provisioning, logging.¹⁰³⁸⁷
• 10x: learning control health scores and dynamic sampling; anomaly detection over control telemetry and user behavior.⁸⁹⁸⁸

3.1.3 Evidence lifecycle automation

• Auto‑collection, tagging to controls, retention policies, and auditor‑ready snapshots.⁹³⁸⁸
• Agents that detect gaps, create tickets, propose fixes, and verify remediation in source systems.¹⁰⁴⁸⁹

3.1.4 Policy, procedure, and contract management

• Template libraries aligned to frameworks and verticals; approval workflows.⁸⁷⁹³
• LLM agents that:
  • Draft policies mapped to your control graph and specific stack.
  • Parse DPAs/BAAs/contracts and auto‑link obligations to controls and risks.⁸⁸⁹³

3.1.5 Risk, vendor, and Trust Center

• Risk register, KRIs, vendor questionnaires, Trust Center for customers.⁹⁰¹⁰²
• Agents that:
  • Read vendor SOC/ISO/DPAs and auto‑score risk and residual controls.
  • Maintain an always‑current Trust Center with live evidence and AI‑answered questionnaires.¹⁰⁴⁸⁹

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4. Agentic platform design (how you 10x)

4.1 Agent roles and coordination

4.1.1 Regulatory intelligence agent

• Monitors regulatory feeds (laws, standards updates, guidance, enforcement actions) and normalizes them.⁹²⁹⁹⁸⁹
• Continuously updates mappings to the internal control ontology and proposes new or changed controls.⁸⁹⁸⁸

4.1.2 Control & posture agent

• Continuously evaluates control health from telemetry; assigns risk‑weighted priorities to gaps.¹⁰³⁸⁸
• Drafts remediation plans, auto‑creates tickets, and, where allowed, executes low‑risk changes (e.g., enforce MFA).¹⁰⁴⁸⁹

4.1.3 Evidence & audit agent

• Ensures every control has sufficient, timely evidence; backfills gaps before audits.¹⁰³⁸⁸
• Assembles audit packages, responds to PBC lists, and manages auditor Q&A with traceability.⁹¹¹⁰⁴

4.1.4 Vendor & third‑party risk agent

• Ingests vendor artifacts (SOC reports, pen tests, DPAs) and security pages; extracts obligations and residual risk.¹⁰¹¹⁰⁴
• Suggests compensating controls and monitors renewal dates and major vendor changes.

4.1.5 AI governance agent

• Maintains AI system inventory, associated data sources, evaluations, and deployment approvals.⁹⁹⁹⁴⁹⁶
• Checks AI systems against EU AI Act, NIST AI RMF, ISO 42001, FDA/BS 30440 expectations for healthcare and medical devices.⁹⁸⁹⁷⁹⁶

4.2 Agent orchestration patterns

4.2.1 Central graph + event bus

• Compliance graph (controls, assets, users, vendors, regs) plus event bus for changes (new law, new asset, failed control).⁸⁸⁸⁹
• Agents subscribe to particular event types and coordinate via shared state and explicit workflows.

4.2.2 Guardrails and human‑in‑the‑loop

• Policy engine for actions: classify as auto‑apply, suggest‑and‑approve, or advisory‑only based on risk and domain.⁹²⁸⁸
• Detailed logs and explainer views for regulators and auditors to review agent decisions.

4.2.3 Multi‑tenant & vertical abstractions

• Per‑tenant data boundaries with strict isolation and data‑minimizing prompts for LLMs.¹⁰⁵⁹⁹
• “Vertical adapters” that constrain agents to approved actions in finance, healthcare, or medical contexts.

4.2.4 LLM and non‑LLM hybrid

• LLMs for text interpretation, mapping, drafting; classical ML/heuristics for scoring and anomaly detection.⁹³⁸⁸
• RPA/automation for deterministic workflows (e.g., export reports, push config changes).⁸⁸

4.2.5 Validation & evaluation

• Golden‑set test suites per framework and vertical; regression tests for every agent policy change.⁹⁷⁹⁶
• Bias, hallucination, and robustness evaluation for AI agents, particularly in regulated healthcare or financial decisions.⁹⁶⁹³

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5. Economic and disruptive levers

5.1 Cost, UX, and ecosystem

5.1.1 Radically lower delivery cost

• Agents replace large portions of MSP/GRC consultants’ manual work across requirements mapping, documentation, evidence wrangling, and audit prep.⁸⁷⁸⁸
• Usage‑aligned pricing (events, compute, frameworks) with always‑on compliance as a baseline instead of per‑framework tax.

5.1.2 Time‑to‑compliance & expansion

• Onboarding wizards that connect infra, infer baseline controls, and auto‑generate policies in days.¹⁰³⁸⁷
• One‑click “add framework” using cross‑mapped controls and incremental gap analysis instead of new projects.⁸⁹⁸⁷

5.1.3 Vertical accelerators

• Pre‑built packs for fintech, healthcare, medical devices, and pharma with regulator‑friendly templates and standard “evidence stories.”⁹⁸⁹⁵⁹⁶
• Pre‑configured AI governance for high‑risk AI use cases (diagnostics, credit, fraud), mapping to EU AI Act, FDA, and NIST AI RMF.⁹⁴⁹⁹⁹⁶

5.1.4 Open standards & interoperability

• APIs and schema for plugging in external control tests, detection tools, and ticketing systems.^1019389
• Import/export into common formats for auditors, regulators, and internal BI (e.g., data warehouse, lakehouse).

5.1.5 Trust, certification, and assurance

• Platform itself certified to SOC 2, ISO 27001, possibly ISO 42001 and HITRUST where relevant.⁹⁹⁸⁷
• Transparent documentation of AI agents’ design, data usage, and guardrails to meet emerging AI compliance expectations.^10599103

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If useful, a next step could be:

• A control‑graph schema sketch (entities + edges) for an agentic compliance OS, and
• A prioritized feature roadmap (MVP → v1 competitive → v2 10x) tailored to a multi‑agent platform aimed at fintech + healthcare first. ¹⁰⁶

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If useful, a next step could be:

A control‑graph schema sketch (entities + edges) for an agentic compliance OS, and A prioritized feature roadmap (MVP → v1 competitive → v2 10x) tailored to a multi‑agent platform aimed at fintech + healthcare first. Related What core agent capabilities drive continuous compliance automation Detailed risk model components for an agentic compliance platform Standards and certifications required for healthcare AI agents How to architect secure data flows for vendor AI agents Pricing models that enable 10x cost reduction for enterprises' GRC

A control-graph–centric, agentic compliance OS for fintech and healthcare needs a strongly typed graph as the system of record plus agents that reason over it, with a roadmap that moves from “agent-assisted” to “agent-operated” continuous compliance while meeting security/AI governance standards like ISO/IEC 27001 and 42001.^107108109110

Control-graph schema sketch

Think of the core as a multi-tenant compliance knowledge graph with these main entity types and edge patterns.^111112113

Level 1 – Top-level entities

• Organization, BusinessUnit, Product / System
• Regulation / Standard / Framework (e.g., SOC 2, ISO 27001, HIPAA, EU AI Act, ISO 42001)^114115116
• Control, Policy, Procedure
• Asset (CloudResource, Application, Dataset, Model, Device)
• Identity (User, Role, Group, ServiceAccount)
• Risk, Issue / Finding, Test / Check, EvidenceItem, Audit, Vendor / ThirdParty¹¹⁰¹¹²

Level 2 – Key relationships (edges)

• Regulation/Standard → Control: “requires” (with clause metadata, articles, CC IDs, etc.).^117108118
• Control → Policy / Procedure: “implemented-by” (text + process artifacts).¹¹⁹¹¹²
• Control → Test / Check: “verified-by” (continuous controls monitoring).¹²⁰¹¹¹
• Test / Check → Asset / Identity: “runs-on” or “evaluates”.
• EvidenceItem → Test / Control / Audit: “supports” (with timestamp, source integration, retention info).¹⁰⁹¹²¹
• Risk → Control / Asset / Vendor / Model: “mitigated-by” / “impacts”.¹¹²¹¹⁰

Level 3 – Healthcare/fintech/AI specializations

• AIModel, Dataset, AIUseCase, EvaluationRun for AI systems; edges to Regulation (EU AI Act category, FDA device class) and Risk.^118122123
• ClinicalSystem, MedicalDevice, EHRIntegration with links to HIPAA, FDA SaMD, ISO 13485 controls.^124122123
• FinancialService, PaymentFlow, KYCProcess with links to PCI DSS, GLBA, DORA, AML/KYC guidance.^113109114

Level 4 – Agent-facing abstractions

• “Task” nodes (RemediationTask, ReviewTask, AssessmentTask) linked to Control, Risk, and Identity to represent work items for or by agents.¹¹⁹¹¹⁰
• “Playbook” nodes representing reusable workflows (e.g., “Quarterly access review”, “New AI model launch”) that agents can instantiate.¹¹¹¹¹⁹
• “Capability” nodes for each agent (e.g., EvidenceAgent, RegulatoryAgent) with allowed entity/edge types and action scopes for guardrails.¹²⁵¹⁰⁸

Level 5 – Security and lineage

• DataLineage edges from EvidenceItem to SourceSystem and, for AI, from Model to Dataset and DataSource for traceability.^116122118
• AuditLog events for every agent action with pointers into the graph (who/what/when/why) to satisfy AI governance expectations under ISO 42001 and EU AI Act.^108126117

This graph becomes the single substrate agents read/write, enabling explainability: “Control X is red because Check Y on Asset Z failed; required by EU AI Act article A and ISO 42001 clause B.”¹¹⁷¹⁰⁸

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Core agent capabilities for continuous compliance

Map agent roles to graph operations and DevSecOps rails.^109110119

Level 1 – Regulatory intelligence agent

• Monitors regulatory/standards sources (EU AI Act, HIPAA updates, ISO 42001, sector guidance) and parses them into Regulation and Control nodes.^107118117
• Suggests new controls or mapping changes with diffs and impact analysis over the existing graph.

Level 2 – Control health & posture agent

• Continuously consumes telemetry (cloud configs, IdP, MDM, CI/CD, logs) to update Test nodes and control statuses.^120109111
• Computes posture scores per framework, product, and high-risk AI system; surfaces prioritized Issues and RemediationTasks.

Level 3 – Evidence & audit agent

• Ensures every in-scope control has current EvidenceItems from trusted sources; detects stale or missing evidence.¹²¹¹⁰⁹
• Assembles audit packages and PBC responses by traversing Regulation → Control → Evidence and generating human-readable “evidence stories.”

Level 4 – Risk & vendor agent

• Continuously updates Risk nodes based on posture, incidents, vendor changes, and AI system behavior.^110112119
• Reads vendor SOC/ISO reports, DPAs, and security docs to auto-populate Vendor risk and obligations (e.g., data location, sub-processors).¹²⁷¹²¹

Level 5 – AI governance & assurance agent

• Maintains AIModel inventory with EU AI Act category, ISO 42001 requirements mapping, and FDA/EU MDR implications for healthcare AI.^122123107117
• Tracks model evaluations, bias tests, and performance monitoring; raises Issues when risk thresholds or regulatory constraints are breached.¹¹⁸¹¹⁶

Together these enable “always-on” posture plus audit-readiness and AI risk management, aligning with continuous compliance best practices.^119109111110

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Risk model components for an agentic platform

Your risk model needs to be machine-operable but regulator-friendly.^112107110

Level 1 – Risk taxonomy

• Operational, security, privacy, regulatory, financial, AI/algorithmic, clinical (for healthcare), third-party/vendor risk.^128107118
• Each Risk node has type, subtype, linked Control(s), Asset(s), and Regulation references.

Level 2 – Scoring dimensions

• Likelihood, impact (multi-dimensional: financial, clinical, safety, reputational), detectability; plus velocity/time-to-impact.¹⁰⁷¹¹²
• For AI risk: add model criticality, data sensitivity, bias/harms potential, explainability, robustness.^116122118

Level 3 – Inputs and signals

• Control failures, incidents, near-misses, vulnerability data, vendor changes, monitoring anomalies, model drift alerts.^111110119
• Regulatory changes and enforcement actions that increase inherent risk for certain activities or AI use cases.¹¹⁸¹⁰⁷

Level 4 – Treatment and residual risk

• Link TreatmentPlan / RemediationTask nodes to Risk, plus deadline, owner, and expected residual score.¹¹²¹¹⁹
• Agents simulate “what-if” effects of planned treatments on overall risk posture for frameworks and AI governance.¹⁰⁹¹¹⁹

Level 5 – Reporting & attestations

• Risk register, heatmaps, KRIs, and attestations per framework (e.g., ISO 42001 AI risk treatment requirements, HIPAA risk analysis).^108117107
• Explainable chain from high-level risk reports down to specific EvidenceItems and control tests.

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Standards & certifications for healthcare AI agents

For fintech + healthcare/med devices/pharma you’ll want to design for compliance with traditional security plus AI- and sector-specific regimes.^122128107

Level 1 – Security & privacy foundations

• ISO/IEC 27001 (ISMS), SOC 2, possibly HITRUST for healthcare, with HIPAA/HITECH controls where PHI is handled.¹¹⁴¹²⁸
• GDPR/CCPA/CPRA for data protection, including DPIA support.¹¹⁵¹²⁷

Level 2 – AI management & governance

• ISO/IEC 42001 AI management system to structure AI governance, risk, transparency, and monitoring.^117108107
• NIST AI Risk Management Framework for AI risk categorization and treatment.¹¹⁶¹¹⁸

Level 3 – Healthcare and medical device specifics

• FDA: AI SaMD guidance, good machine learning practice (GMLP), post-market surveillance for AI-enabled devices.^123129118
• EU MDR/IVDR and EU AI Act requirements for high-risk medical AI systems (clinical evaluation, logging, human oversight).^124122118

Level 4 – Data & quality management

• ISO 13485 for medical device quality management and linkage to AI components where part of devices.¹¹⁷¹¹⁸
• Data governance controls for PHI and clinical data (provenance, lineage, retention, de-identification).¹²⁸¹¹⁸

Level 5 – Platform-level AI assurances

• Documentation of agents’ roles, limits, and oversight mechanisms aligned with ISO 42001 and EU AI Act transparency/record-keeping.^126108117
• Periodic independent audits or certifications for the AI management system once mature.¹⁰⁷¹¹⁷

---

Secure data flows for vendor AI agents

Your architecture needs strong segmentation and explicit data contracts.^125109117

Level 1 – Data zones

• Separate zones: Customer Data Plane, Metadata/Graph Plane, Agent Runtime Plane; add a Restricted PHI/PCI zone for sensitive workloads.^128118111
• Strict tenancy isolation across all zones with per-tenant encryption and key management.

Level 2 – Data minimization & contracts

• Clear schemas for what agent calls can include: minimal fields, no raw PHI/PCI unless in approved flows, tokenization where possible.¹²⁸¹⁰⁷
• Policy engine enforcing which entities/attributes can cross planes and which agents can access them.

Level 3 – Agent execution & guardrails

• Agents invoked via an orchestrator that enforces policy checks, redacts/transforms inputs, and logs full context and outputs.¹²⁵¹¹¹
• For third-party LLMs, use retrieval-style prompting (only derived metadata, no raw sensitive data) and regional routing where required by law.¹¹⁵¹⁰⁷

Level 4 – Monitoring and incident response

• Data access logs and anomaly detection for unusual agent behavior or data flows.¹¹⁹¹¹¹
• Pre-defined playbooks when agents misbehave or an integration is compromised (auto revoke keys, rotate credentials, freeze certain capabilities).

Level 5 – Customer and regulator transparency

• Diagrams and documents describing data flows and agent behavior, aligned with ISO 42001 communication and EU AI Act transparency expectations.^126108117
• Tenant-level controls to disable certain agents or restrict them to read-only/reporting modes.

---

Pricing models for 10x cost reduction

To undercut legacy GRC vendors, you want to avoid per-seat/per-framework creep and align price with automated value.^130131132

Level 1 – Current landscape constraints

• Many GRC tools charge per user, per framework, and per module, often tied to employee count, leading to $10k–$30k+ per year for small environments.¹³¹¹³⁰
• Hidden fees around additional frameworks, audits, and connectors are common.¹³²¹³¹

Level 2 – Value-aligned metrics

• Number of managed assets/systems, number of active frameworks, and volume of evidence/controls monitored.¹²¹¹⁰⁹
• For AI governance, number of models/use cases classified as high-risk under EU AI Act or similar.¹¹⁸¹⁰⁷

Level 3 – Pricing structures

• Flat “all-frameworks-included” per-organization tier for SBB/SMB, capped by asset or Employee Count band.¹³³¹³¹
• Usage-based add-ons for high-volume evidence processing or very large models/integrations.^134135133

Level 4 – Bundling by vertical

• Fintech bundle: security + privacy + PCI + DORA + AI governance base, single price per year per organization.¹¹³¹¹⁴
• Healthcare bundle: HIPAA + ISO 27001 + AI governance + optional FDA/MDR pack; pricing based on number of clinical systems and AI models.^124118128

Level 5 – Disruption levers

• No-per-user pricing for internal users; unlimited internal stakeholders to drive adoption.^133131132
• “Done-for-you” partner services layered on top (MSPs, auditors) who rely on your agentic platform, driving ecosystem lock-in while you keep platform pricing lean.¹²¹¹¹⁴

---

If you want to go deeper, next prompts could be:

• “Propose a concrete property/edge schema (with example JSON) for the control graph for SOC 2 + HIPAA + EU AI Act.”
• “Design an MVP → v1 competitive → v2 10x roadmap with specific agent stories and milestones for a fintech + healthcare launch.” ¹³⁶

⁂

---

Related

Sketch a control‑graph schema for an agentic compliance OS focusing on entities and edges Map required agent types and their core capabilities for continuous compliance automation Design a prioritized feature roadmap MVP to v2 for a multi‑agent platform in fintech and healthcare Define data governance and secure data flow patterns for vendor AI agents in healthcare List required standards and certifications for healthcare AI agents and timelines to achieve them

A multi‑agent, control‑graph–centric compliance OS for fintech and healthcare needs: (1) a graph schema that cleanly models controls, assets, AI systems, and regulations; (2) a small set of powerful agent types; (3) a staged roadmap; (4) strict data governance patterns; and (5) a standards plan (SOC 2/ISO 27001 → HIPAA/HITRUST → ISO 42001/AI regs).^{137138139140141}

---

Control‑graph schema (entities & edges)

At a high level, model compliance as a graph of regulations, controls, assets, people, and evidence.^142143144

• Core entities
  • Regulation / Framework: SOC 2, ISO 27001, HIPAA, PCI DSS, GDPR, EU AI Act, ISO 42001, NIST AI RMF.^145146147
  • Control: atomic, implementation‑agnostic requirement (e.g., “MFA on admin accounts”).
  • Policy, Procedure, Playbook: documented rules and operational workflows.
  • Asset: CloudResource (AWS/GCP/Azure), Application, Dataset, AIModel, MedicalDevice, ClinicalSystem.^148149150
  • Identity: User, Role, Group, ServiceAccount; plus Org, BusinessUnit, Product/System.
  • Test / Check, EvidenceItem, Risk, Issue/Finding, Task (RemediationTask, ReviewTask, AssessmentTask), Audit, Vendor / ThirdParty.^140141142
• Key edges
  • Regulation → Control: “requires” (with citations to articles/clauses/CC IDs).¹³⁸¹⁵⁰
  • Control ↔ Control: “duplicates/overlaps” to enable cross‑framework mapping.
  • Control → Policy / Procedure / Playbook: “implemented‑by”.¹⁵¹
  • Control → Test / Check: “verified‑by” (continuous controls monitoring).¹⁴⁴¹⁵²
  • Test / Check → Asset / Identity: “runs‑on” / “evaluates”.
  • EvidenceItem → Test / Control / Audit: “supports” (includes source system, timestamp, retention).¹⁵³¹⁴⁰
  • Risk → Control / Asset / Vendor / AIModel: “mitigated‑by” / “impacts”.¹⁴¹¹⁴²
  • Task → Issue / Risk / Control: “resolves” / “implements”.
  • Vendor → Asset / DataFlow: “provides” / “processes‑data‑from”.
• Healthcare/AI specializations
  • AIModel ↔ Dataset / DataSource: “trained‑on” / “uses”, for AI traceability.^147150148
  • AIModel → Regulation (EU AI Act, FDA SaMD): “classified‑as” (e.g., high‑risk) and “subject‑to”.^149150148
  • ClinicalSystem / MedicalDevice → Regulation (HIPAA, MDR, FDA): “subject‑to”.^154155148
  • DataFlow edges: Source → Destination with attributes (data types, PHI/PII flags, jurisdiction, processor/sub‑processor info).^146150154
• Agent guardrail layer
  • AgentType (RegulatoryAgent, PostureAgent, EvidenceAgent, RiskAgent, AIGovernanceAgent) nodes.
  • AgentType → EntityType/EdgeType: “allowed‑to‑read/write”, forming policy for what each agent can touch.¹³⁹¹⁵⁶
  • AuditLog events referencing agents + graph nodes for explainability and AI governance under ISO 42001/EU AI Act.^157138139

---

Agent types & core capabilities

Align agents with continuous compliance components and AI governance.^140141151

• Regulatory intelligence agent
  • Continuously ingests updates on SOC/ISO/HIPAA/PCI, EU AI Act, FDA, ISO 42001, etc., and creates/updates Regulation and Control nodes.^150137138
  • Maintains cross‑framework mappings and impact diffs (“X new obligations, affects Y controls and Z systems”).¹⁴³¹⁴²
• Posture & control health agent
  • Consumes telemetry from cloud, IdP, MDM, CI/CD, EHR/clinical systems to update Test and Control status.^152144140
  • Computes posture scores per framework, product, and AI system; raises Issues and RemediationTasks with risk‑based priority.¹⁴¹¹⁵¹
• Evidence & audit agent
  • Ensures every in‑scope control has fresh EvidenceItems; detects missing/stale evidence and schedules pulls.¹⁵³¹⁴⁰
  • Assembles audit packages from graph traversals and generates human‑readable “evidence narratives” for auditors/regulators.¹⁵⁸¹⁵⁹
• Risk & vendor agent
  • Maintains Risk nodes based on posture failures, incidents, vendor changes, and AI model behavior.^142151141
  • Parses vendor SOC/ISO reports, DPAs, BAAs, and security docs to auto‑populate Vendor risk, obligations, and DataFlow edges.¹⁶⁰¹⁵³
• AI governance & healthcare agent
  • Maintains AIModel inventory (purpose, risk category, training data, evaluations) and maps to EU AI Act and ISO 42001 requirements.^138147148
  • Tracks clinical/medical AI constraints (FDA SaMD guidance, MDR/IVDR) and updates Risks and Tasks when non‑conformities appear.^155149150
• Workflow & orchestration agent
  • Converts agent findings into work: creates/reassigns Tasks, sequences Playbooks (e.g., access reviews, DPIAs, AI impact assessments).¹⁴⁴¹⁵¹
  • Coordinates human‑in‑the‑loop approvals and enforces action guardrails.

---

Feature roadmap: MVP → v1 → v2 (fintech & healthcare)

Design for quick vertical wins with a clear evolution to agent‑operated compliance.^145154140141

• MVP (0–9 months) – “Agent‑assisted”
  • Graph & integrations: multi‑tenant control graph; integrations for AWS/GCP, Okta/Entra/Google Workspace, major HRIS and MDM; basic EvidenceItems and Tests.¹⁵²¹⁴⁵
  • Frameworks: SOC 2, ISO 27001, HIPAA core; basic EU AI Act + ISO 42001 mapping for AI inventory.^146154138
  • Agents: Posture & Evidence agents in read‑only/assist mode; workflows push into Jira/ServiceNow for remediation.¹⁵¹¹⁴⁰
  • Fintech/healthcare: PCI DSS scoping (without full automation), HIPAA technical safeguards, AIModel inventory for clinical/fintech use cases.^143148154
  • Security: start SOC 2 readiness, baseline data segregation, logging, and encryption controls.¹⁶¹¹⁵³
• v1 competitive (9–18 months) – “Always‑on posture”
  • Frameworks: add PCI DSS 4.0, NIST CSF/800‑53, DORA, NIS2, healthcare MDR/IVDR mappings, deeper EU AI Act obligations.^150143145
  • Agents: Regulatory intelligence, Risk & vendor, AI governance agents; posture agent begins auto‑creating low‑risk remediations (e.g., ticket creation, non‑disruptive config checks).^137140151
  • Features: access reviews, Trust Center, DPIA/DORA/AI impact assessment workflows; vendor AI assessment flows for healthcare SaaS.^154144146
  • Certs: achieve SOC 2 Type II, ISO 27001; HIPAA‑aligned hosting; start ISO 42001 design and NIST AI RMF alignment.^137138154
• v2 10x (18–36 months) – “Agent‑operated compliance ops”
  • Agent autonomy: posture/evidence/AI agents execute remediations within policy (e.g., enforce MFA, update config, rotate keys) for low‑risk domains; human approval for higher‑risk/clinical contexts.¹⁵⁶¹⁴⁰
  • AI governance: full ISO 42001 certification; robust EU AI Act support for high‑risk healthcare AI (documentation, logs, human oversight, post‑market monitoring).^138150137
  • Vertical packs: turnkey fintech and healthcare bundles including PCI + DORA and HIPAA + MDR + AI governance with pre‑built Playbooks and evidence templates.^155145154
  • Pricing: asset/framework‑bundled or org‑flat pricing with unlimited internal users and aggressive all‑framework bundles for SBB/SMB.^162163161
  • Assurance: consider HITRUST (if customer‑driven), independent AI governance assessments, and regulator‑friendly design docs for agents.^154137138

---

Data governance & secure flows for vendor AI agents (healthcare)

You need strict zoning, minimization, and oversight to satisfy HIPAA, GDPR, and AI governance expectations.^146137138154

• Zoning & isolation
  • Separate planes: Customer Data Plane (source systems, PHI), Metadata/Graph Plane (controls, risks, references), Agent Runtime Plane; add dedicated restricted zones for PHI/clinical data.^144150154
  • Per‑tenant encryption, access control, and routing; regional segregation for EU healthcare workloads.¹⁵⁰¹⁴⁶
• Minimization & contracts
  • Explicit schemas define what each agent sees; prefer metadata and derived stats over raw PHI/PII; tokenize or de‑identify wherever possible.¹³⁷¹⁵⁴
  • Data processing agreements and BAAs with vendor LLMs/sub‑processors that match HIPAA/GDPR requirements.¹⁴⁶¹⁵⁴
• Guardrails & observability
  • Orchestrator enforcing agent policies: redaction, field‑level filters, allowed action sets; all agent calls logged with context and outputs.¹⁵⁶¹⁴⁴
  • Monitoring for anomalous agent behavior or over‑broad data access, tied into incident response playbooks.¹⁴¹¹⁵¹
• AI governance alignment
  • Documentation of data flows, agent behavior, and oversight consistent with ISO 42001 and EU AI Act transparency/record‑keeping.^139157138
  • Regular reviews of AI agents’ performance, drift, and error modes, especially for clinical decision support or high‑risk AI.¹⁵⁰¹³⁷

---

Required standards & healthcare AI certifications (with rough timelines)

Exact timing depends on resources, but a staged plan aligned with expectations for healthcare/fintech AI looks like this.^145154137

• Phase 1 (0–18 months) – foundations
  • SOC 2 Type II and ISO 27001 for the platform’s ISMS (high trust signal for all verticals).^161153145
  • HIPAA alignment (and BAAs) if handling PHI; GDPR/CCPA compliance with DPIA capabilities.^160154146
• Phase 2 (18–30 months) – AI & healthcare uplift
  • ISO/IEC 42001 AI management system certification to formalize AI governance and agent oversight.^157139138
  • NIST AI RMF implementation aligning risk model and processes for AI agents.¹⁴⁷¹⁵⁰
  • For heavy healthcare focus: consider HITRUST or equivalent for stronger assurances to hospitals/health systems.¹⁵³¹⁵⁴
• Phase 3 (30–48 months) – advanced clinical/medical AI
  • For AI used in or supporting regulated medical devices: demonstrate conformity to FDA SaMD expectations, GMLP, and, for EU, MDR/IVDR plus EU AI Act high‑risk obligations (logs, human oversight, post‑market monitoring).^148149155150
  • Independent AI governance and safety assessments, used as differentiators with healthcare regulators and large payers/providers.¹³⁸¹³⁷

If you want concrete artifacts next, a good follow‑up is: “Provide an example JSON schema fragment for Control, Asset, AIModel, Regulation, Risk, and EvidenceItem aligned with this graph design, plus one end‑to‑end example path (EU AI Act → AIModel → Evidence).”

⁂

---

Provide an example JSON schema fragment for Control, Asset, AIModel, Regulation, Risk, and EvidenceItem aligned with this graph design, plus one end‑to‑end example path (EU AI Act → AIModel → Evidence).”

Below is a concrete JSON-shaped data model fragment plus an end‑to‑end example path for a high‑risk EU AI Act healthcare use case. Names and fields are illustrative but aligned with common EU AI Act requirements.^{164165166167168}

---

JSON schema fragments (core entities)

These are not full JSON Schema drafts, but strongly typed fragments you can turn into JSON Schema/Protobuf/ORM models.

Regulation

{
  "Regulation": {
    "id": "reg-eu-ai-act",
    "name": "EU Artificial Intelligence Act",
    "jurisdiction": "EU",
    "type": "AI",
    "version": "2024",
    "url": "https://artificialintelligenceact.eu/",
    "articles": [
      {
        "id": "reg-eu-ai-act-art-9",
        "article_number": "9",
        "title": "Risk management system",
        "summary": "High-risk AI systems must have a continuous risk management system across the lifecycle.",
        "obligations": ["RISK_MANAGEMENT_SYSTEM"]
      },
      {
        "id": "reg-eu-ai-act-art-12",
        "article_number": "12",
        "title": "Record-keeping",
        "summary": "High-risk AI systems must enable automatic logging.",
        "obligations": ["LOGGING", "RECORD_KEEPING"]
      }
    ],
    "metadata": {
      "risk_classification_rules": ["ANNEX_I", "ANNEX_III"],
      "high_risk_definition_url": "https://artificialintelligenceact.eu/article/6/"
    }
  }
}

Control

{
  "Control": {
    "id": "ctrl-eu-ai-act-logging",
    "name": "High-risk AI logging and record-keeping",
    "description": "Ensure automatic logging of relevant events for high-risk AI systems and retain logs for at least the required period.",
    "category": "AI_OPERATIONS",
    "status": "IN_SCOPE",
    "regulation_refs": [
      {
        "regulation_id": "reg-eu-ai-act",
        "article_id": "reg-eu-ai-act-art-12",
        "clauses": ["12(1)", "12(2)"]
      }
    ],
    "overlaps_with": [
      "ctrl-gdpr-logging",
      "ctrl-iso42001-logging"
    ],
    "implementation_refs": [
      {
        "type": "POLICY",
        "id": "pol-ai-logging-policy"
      },
      {
        "type": "PLAYBOOK",
        "id": "pbk-ai-log-review-quarterly"
      }
    ]
  }
}

Asset

A generic asset with AI specializations handled via AIModel:

{
  "Asset": {
    "id": "asset-api-eu-clinical-imaging",
    "name": "EU Clinical Imaging Inference API",
    "type": "Application",
    "environment": "production",
    "business_owner": "bu-eu-radiology",
    "technical_owner": "team-ml-platform",
    "data_classification": ["PHI", "PII", "HEALTH_DATA"],
    "regions": ["eu-central-1", "eu-west-1"],
    "linked_models": ["aimodel-rad-ct-diag-v3"],
    "integrations": [
      {
        "type": "CLOUD",
        "provider": "AWS",
        "resource_id": "arn:aws:ecs:eu-central-1:123:service/rad-inference"
      }
    ]
  }
}

AIModel

{
  "AIModel": {
    "id": "aimodel-rad-ct-diag-v3",
    "name": "CT Scan Diagnostic Model v3",
    "provider": "internal",
    "intended_use": "Assist radiologists in detecting pulmonary embolism from CT scans.",
    "domain": "HEALTHCARE_DIAGNOSTIC",
    "risk_category": "HIGH_RISK",
    "high_risk_basis": {
      "eu_ai_act_annex": "ANNEX_III",
      "annex_section": "Medical devices and safety components",
      "reference_url": "https://artificialintelligenceact.eu/annex/3/"
    },
    "regulation_refs": [
      {
        "regulation_id": "reg-eu-ai-act",
        "applicable_articles": ["9", "10", "11", "12", "13", "14", "15"]
      }
    ],
    "data_sources": [
      {
        "dataset_id": "dataset-ct-eu-2023",
        "role": "TRAINING"
      },
      {
        "dataset_id": "dataset-ct-eu-2024-val",
        "role": "VALIDATION"
      }
    ],
    "deployment_assets": ["asset-api-eu-clinical-imaging"],
    "human_oversight": {
      "required": true,
      "supervising_role": "Board-certified radiologist",
      "override_capability": true
    },
    "lifecycle": {
      "version": "3.1.0",
      "status": "IN_PRODUCTION",
      "last_validated_at": "2025-10-01T12:00:00Z"
    }
  }
}

Risk

{
  "Risk": {
    "id": "risk-ai-eu-pe-misdiagnosis",
    "title": "Misdiagnosis of pulmonary embolism by CT AI model",
    "type": "AI_SAFETY",
    "domain": "CLINICAL",
    "description": "Incorrect classification of CT scans leading to missed or false diagnosis of pulmonary embolism in EU patients.",
    "linked_assets": ["asset-api-eu-clinical-imaging"],
    "linked_models": ["aimodel-rad-ct-diag-v3"],
    "linked_controls": ["ctrl-eu-ai-act-logging", "ctrl-eu-ai-act-risk-mgmt"],
    "linked_regulations": ["reg-eu-ai-act"],
    "likelihood": 3,
    "impact": {
      "clinical": 5,
      "regulatory": 4,
      "reputational": 4
    },
    "inherent_score": 15,
    "residual_score": 9,
    "status": "OPEN",
    "treatment_plan": {
      "tasks": ["task-add-additional-human-review", "task-improve-monitoring-metrics"],
      "target_residual_score": 6
    }
  }
}

EvidenceItem

{
  "EvidenceItem": {
    "id": "evid-aimodel-rad-ct-diag-v3-logs-2025q4",
    "type": "LOG_EXPORT",
    "description": "Q4 2025 log export for high-risk AI diagnostic model, including inference events and human override actions.",
    "source_system": "AWS_CloudWatch",
    "asset_id": "asset-api-eu-clinical-imaging",
    "model_id": "aimodel-rad-ct-diag-v3",
    "control_ids": ["ctrl-eu-ai-act-logging"],
    "regulation_refs": [
      {
        "regulation_id": "reg-eu-ai-act",
        "article_number": "12"
      }
    ],
    "time_range": {
      "start": "2025-10-01T00:00:00Z",
      "end": "2025-12-31T23:59:59Z"
    },
    "location": {
      "bucket": "compliance-eu-logs",
      "object_key": "ai/high-risk/ct-diag-v3/2025Q4/logs.jsonl",
      "region": "eu-central-1"
    },
    "collected_at": "2026-01-05T10:00:00Z",
    "collected_by": "agent-evidence-collector",
    "checksum": "sha256-...",
    "retention_until": "2026-07-05T10:00:00Z",
    "metadata": {
      "log_schema_version": "1.2",
      "contains_phi": true,
      "access_controls": ["ROLE_COMPLIANCE_OFFICER_EU", "ROLE_AUDITOR_EU"]
    }
  }
}

---

End‑to‑end example path: EU AI Act → AIModel → Evidence

This illustrates one concrete graph walk your agents would execute for an EU high‑risk healthcare system.^167165169166

1. Regulation → Control
   • Start at Regulation.id = "reg-eu-ai-act".
   • Follow articles[article_number="12"] and its obligation LOGGING.
   • Resolve to Control.id = "ctrl-eu-ai-act-logging" via the regulation_refs link (Regulation/Article “requires” Control). This encodes the EU AI Act requirement that high‑risk AI systems provide automatic logging and record‑keeping.^170168165166
2. Control → AIModel
   • From Control "ctrl-eu-ai-act-logging", traverse edges to all AI models in scope (e.g., “applies-to” AIModel).
   • One such node is AIModel.id = "aimodel-rad-ct-diag-v3", which is marked risk_category = "HIGH_RISK" and linked to reg-eu-ai-act in regulation_refs.
   • This expresses that this diagnostic model is a high‑risk AI system under the EU AI Act (Annex III healthcare use case).^171169172167
3. AIModel → Asset(s)
   • From AIModel "aimodel-rad-ct-diag-v3", follow deployment_assets to Asset "asset-api-eu-clinical-imaging".
   • This shows where the high‑risk model is actually running (EU clinical imaging API serving CT inferences).¹⁷³¹⁶⁹
4. Control / AIModel → EvidenceItem
   • From the control and model, query EvidenceItem where control_ids includes "ctrl-eu-ai-act-logging" and model_id = "aimodel-rad-ct-diag-v3" and time_range covers the audit period.
   • Retrieve EvidenceItem "evid-aimodel-rad-ct-diag-v3-logs-2025q4" representing the required logs for record‑keeping (Art. 12).^168165166174
5. EvidenceItem → Risk & audit story
   • Link EvidenceItem back to Risk "risk-ai-eu-pe-misdiagnosis" and to the relevant Audit object (e.g., 2025 EU AI Act conformity assessment).
   • An evidence agent can now produce a narrative: this high‑risk AI diagnostic system is subject to EU AI Act logging obligations; it runs on the EU clinical imaging API; and automatic logs have been collected and retained for the required timeframe, supporting conformity with Article 12.^175166170

You can drop these fragments directly into a graph DB (e.g., nodes with these JSON blobs as properties) or convert them into JSON Schema/OpenAPI for your platform contracts; the key is that every path from Regulation → Control → AIModel/Asset → Evidence is machine‑navigable and explainable for auditors and regulators.^{176177178 179180181182183}

⁂

---

Show a JSON schema for Control including validations and relations

Provide Asset JSON schema with identifiers and lifecycle fields Define AIModel schema with versioning trainingData and lineage Draft Regulation schema for EU AI Act articles and annex links Create Risk schema mapping severity likelihood and mitigations to controlsKPIIDssuckstoo long but must be under70 chars — adjust

Below are compact JSON Schema drafts (Draft 7–ish) for each core type, tuned for implementation, not legal exactness.^184185186187

---

Control schema (with relations)

{
  "$id": "https://example.com/schemas/control.json",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Control",
  "type": "object",
  "required": ["id", "name", "status", "category"],
  "properties": {
    "id": {
      "type": "string",
      "pattern": "^ctrl-[a-z0-9-]+$"
    },
    "name": {
      "type": "string",
      "maxLength": 256
    },
    "description": {
      "type": "string",
      "maxLength": 4000
    },
    "category": {
      "type": "string",
      "enum": [
        "ACCESS_CONTROL",
        "AI_OPERATIONS",
        "CHANGE_MANAGEMENT",
        "LOGGING_MONITORING",
        "DATA_PROTECTION",
        "RISK_MANAGEMENT",
        "OTHER"
      ]
    },
    "status": {
      "type": "string",
      "enum": ["IN_SCOPE", "OUT_OF_SCOPE", "DEPRECATED"]
    },
    "regulation_refs": {
      "type": "array",
      "items": {
        "type": "object",
        "required": ["regulation_id"],
        "properties": {
          "regulation_id": {
            "type": "string",
            "pattern": "^reg-[a-z0-9-]+$"
          },
          "article_id": { "type": "string" },
          "clauses": {
            "type": "array",
            "items": { "type": "string", "maxLength": 32 }
          }
        },
        "additionalProperties": false
      }
    },
    "overlaps_with": {
      "type": "array",
      "items": {
        "type": "string",
        "pattern": "^ctrl-[a-z0-9-]+$"
      }
    },
    "implementation_refs": {
      "type": "array",
      "items": {
        "type": "object",
        "required": ["type", "id"],
        "properties": {
          "type": {
            "type": "string",
            "enum": ["POLICY", "PROCEDURE", "PLAYBOOK", "RUNBOOK"]
          },
          "id": { "type": "string" }
        },
        "additionalProperties": false
      }
    },
    "kpi_ids": {
      "description": "Linked KPI identifiers.",
      "type": "array",
      "items": { "type": "string", "maxLength": 64 }
    },
    "tags": {
      "type": "array",
      "items": { "type": "string", "maxLength": 64 }
    }
  },
  "additionalProperties": false
}

---

Asset schema (identifiers + lifecycle)

{
  "$id": "https://example.com/schemas/asset.json",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Asset",
  "type": "object",
  "required": ["id", "name", "type", "environment"],
  "properties": {
    "id": {
      "type": "string",
      "pattern": "^asset-[a-z0-9-]+$"
    },
    "external_ref": {
      "description": "Cloud or CMDB identifier.",
      "type": "string",
      "maxLength": 512
    },
    "name": {
      "type": "string",
      "maxLength": 256
    },
    "type": {
      "type": "string",
      "enum": [
        "Application",
        "Service",
        "CloudResource",
        "Database",
        "Dataset",
        "MedicalDevice",
        "ClinicalSystem",
        "Other"
      ]
    },
    "environment": {
      "type": "string",
      "enum": ["dev", "test", "staging", "prod"]
    },
    "business_owner_id": {
      "type": "string",
      "maxLength": 128
    },
    "technical_owner_id": {
      "type": "string",
      "maxLength": 128
    },
    "data_classification": {
      "type": "array",
      "items": {
        "type": "string",
        "enum": ["PUBLIC", "INTERNAL", "CONFIDENTIAL", "PII", "PHI", "PAYMENT"]
      }
    },
    "regions": {
      "type": "array",
      "items": { "type": "string", "maxLength": 64 }
    },
    "lifecycle": {
      "type": "object",
      "required": ["status", "created_at"],
      "properties": {
        "status": {
          "type": "string",
          "enum": ["PLANNED", "ACTIVE", "DEPRECATED", "RETIRED"]
        },
        "created_at": {
          "type": "string",
          "format": "date-time"
        },
        "last_modified_at": {
          "type": "string",
          "format": "date-time"
        },
        "retired_at": {
          "type": "string",
          "format": "date-time"
        }
      },
      "additionalProperties": false
    },
    "linked_model_ids": {
      "type": "array",
      "items": {
        "type": "string",
        "pattern": "^aimodel-[a-z0-9-]+$"
      }
    },
    "tags": {
      "type": "array",
      "items": { "type": "string", "maxLength": 64 }
    }
  },
  "additionalProperties": false
}

---

AIModel schema (versioning, trainingData, lineage)

{
  "$id": "https://example.com/schemas/ai_model.json",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "AIModel",
  "type": "object",
  "required": ["id", "name", "version", "risk_category", "intended_use"],
  "properties": {
    "id": {
      "type": "string",
      "pattern": "^aimodel-[a-z0-9-]+$"
    },
    "name": {
      "type": "string",
      "maxLength": 256
    },
    "version": {
      "type": "string",
      "pattern": "^[0-9]+\\.[0-9]+\\.[0-9]+$"
    },
    "provider": {
      "type": "string",
      "enum": ["internal", "vendor", "opensource"]
    },
    "intended_use": {
      "type": "string",
      "maxLength": 2000
    },
    "domain": {
      "type": "string",
      "enum": [
        "HEALTHCARE_DIAGNOSTIC",
        "HEALTHCARE_OPERATIONAL",
        "FINANCIAL_RISK",
        "PAYMENTS",
        "GENERIC",
        "OTHER"
      ]
    },
    "risk_category": {
      "type": "string",
      "enum": ["MINIMAL", "LIMITED", "HIGH_RISK", "UNACCEPTABLE"]
    },
    "eu_ai_act": {
      "type": "object",
      "properties": {
        "is_in_scope": { "type": "boolean" },
        "high_risk_basis": { "type": "string", "maxLength": 512 },
        "annex_reference": {
          "type": "string",
          "enum": ["ANNEX_I", "ANNEX_III", "OTHER", "NONE"]
        }
      },
      "additionalProperties": false
    },
    "training_data": {
      "type": "array",
      "items": {
        "type": "object",
        "required": ["dataset_id", "role"],
        "properties": {
          "dataset_id": {
            "type": "string",
            "pattern": "^dataset-[a-z0-9-]+$"
          },
          "role": {
            "type": "string",
            "enum": ["TRAINING", "VALIDATION", "TEST"]
          },
          "contains_phi": { "type": "boolean" },
          "jurisdictions": {
            "type": "array",
            "items": { "type": "string", "maxLength": 64 }
          }
        },
        "additionalProperties": false
      }
    },
    "lineage": {
      "type": "object",
      "properties": {
        "parent_model_ids": {
          "type": "array",
          "items": { "type": "string", "pattern": "^aimodel-[a-z0-9-]+$" }
        },
        "base_model_name": { "type": "string", "maxLength": 256 },
        "fine_tuning_method": {
          "type": "string",
          "maxLength": 256
        }
      },
      "additionalProperties": false
    },
    "deployment_asset_ids": {
      "type": "array",
      "items": { "type": "string", "pattern": "^asset-[a-z0-9-]+$" }
    },
    "lifecycle": {
      "type": "object",
      "required": ["status"],
      "properties": {
        "status": {
          "type": "string",
          "enum": ["EXPERIMENTAL", "VALIDATION", "IN_PRODUCTION", "RETIRED"]
        },
        "last_validated_at": { "type": "string", "format": "date-time" }
      },
      "additionalProperties": false
    }
  },
  "additionalProperties": false
}

---

Regulation schema (EU AI Act)

{
  "$id": "https://example.com/schemas/regulation.json",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Regulation",
  "type": "object",
  "required": ["id", "name", "jurisdiction", "version"],
  "properties": {
    "id": {
      "type": "string",
      "pattern": "^reg-[a-z0-9-]+$"
    },
    "name": {
      "type": "string",
      "maxLength": 256
    },
    "jurisdiction": {
      "type": "string",
      "maxLength": 64
    },
    "type": {
      "type": "string",
      "enum": ["SECURITY", "PRIVACY", "AI", "HEALTHCARE", "FINANCIAL", "OTHER"]
    },
    "version": {
      "type": "string",
      "maxLength": 32
    },
    "url": {
      "type": "string",
      "format": "uri"
    },
    "articles": {
      "type": "array",
      "items": {
        "type": "object",
        "required": ["id", "article_number"],
        "properties": {
          "id": { "type": "string" },
          "article_number": { "type": "string", "maxLength": 16 },
          "title": { "type": "string", "maxLength": 512 },
          "summary": { "type": "string", "maxLength": 4000 },
          "keywords": {
            "type": "array",
            "items": { "type": "string", "maxLength": 64 }
          }
        },
        "additionalProperties": false
      }
    },
    "annexes": {
      "type": "array",
      "items": {
        "type": "object",
        "required": ["id", "name"],
        "properties": {
          "id": { "type": "string" },
          "name": { "type": "string", "maxLength": 256 },
          "url": { "type": "string", "format": "uri" }
        },
        "additionalProperties": false
      }
    }
  },
  "additionalProperties": false
}

---

Risk schema (severity, likelihood, mitigations → controls)

{
  "$id": "https://example.com/schemas/risk.json",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Risk",
  "type": "object",
  "required": ["id", "title", "type", "likelihood", "severity"],
  "properties": {
    "id": {
      "type": "string",
      "pattern": "^risk-[a-z0-9-]+$"
    },
    "title": {
      "type": "string",
      "maxLength": 256
    },
    "type": {
      "type": "string",
      "enum": [
        "SECURITY",
        "PRIVACY",
        "AI_SAFETY",
        "CLINICAL",
        "FINANCIAL",
        "OPERATIONAL",
        "THIRD_PARTY"
      ]
    },
    "description": {
      "type": "string",
      "maxLength": 4000
    },
    "likelihood": {
      "description": "1–5, where 5 is most likely.",
      "type": "integer",
      "minimum": 1,
      "maximum": 5
    },
    "severity": {
      "description": "1–5 combined impact score.",
      "type": "integer",
      "minimum": 1,
      "maximum": 5
    },
    "score": {
      "description": "Derived (e.g., likelihood * severity).",
      "type": "integer",
      "minimum": 1
    },
    "linked_control_ids": {
      "type": "array",
      "items": {
        "type": "string",
        "pattern": "^ctrl-[a-z0-9-]+$"
      }
    },
    "linked_asset_ids": {
      "type": "array",
      "items": {
        "type": "string",
        "pattern": "^asset-[a-z0-9-]+$"
      }
    },
    "linked_model_ids": {
      "type": "array",
      "items": {
        "type": "string",
        "pattern": "^aimodel-[a-z0-9-]+$"
      }
    },
    "mitigation_tasks": {
      "type": "array",
      "items": {
        "type": "object",
        "required": ["task_id"],
        "properties": {
          "task_id": { "type": "string", "maxLength": 128 },
          "summary": { "type": "string", "maxLength": 512 },
          "linked_control_id": {
            "type": "string",
            "pattern": "^ctrl-[a-z0-9-]+$"
          }
        },
        "additionalProperties": false
      }
    },
    "kpi_ids": {
      "description": "KPI identifiers monitoring this risk.",
      "type": "array",
      "items": { "type": "string", "maxLength": 64 }
    },
    "status": {
      "type": "string",
      "enum": ["OPEN", "IN_TREATMENT", "ACCEPTED", "CLOSED"]
    }
  },
  "additionalProperties": false
}

If you want, next step can be a tiny example document set instantiating all five schemas for a single EU AI Act high‑risk healthcare model and wired together with IDs. ^{188189190191192193194}

⁂

Footnotes

1. https://www.vanta.com/products/automated-compliance ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷

2. https://www.vanta.com/features ↩ ↩² ↩³ ↩⁴

3. https://www.vanta.com/resources/best-compliance-management-software ↩ ↩² ↩³ ↩⁴

4. https://www.vanta.com/resources/300-integrations ↩

5. https://sprinto.com/blog/oneleet-vs-vanta/ ↩

6. https://www.vanta.com/collection/hipaa/hipaa-and-soc-2 ↩ ↩² ↩³ ↩⁴

7. https://rhymetec.com/vanta-compliance-services-your-complete-compliance-solution/ ↩ ↩²

8. https://www.vanta.com/all-categories/compliance-frameworks ↩

9. https://www.vanta.com/collection/grc/compliance-automation ↩ ↩²

10. https://www.vanta.com/resources/soc-2-compliance-for-healthcare ↩ ↩²

11. https://www.vanta.com/products/additional-frameworks ↩

12. https://www.vanta.com/integrations ↩

13. https://help.vanta.com/en/collections/12575285-integrations ↩

14. https://www.vanta.com/downloads/vanta-in-action-compliance-automation ↩

15. https://www.vanta.com/resources/best-security-questionnaire-automation-software ↩ ↩²

16. https://venturebeat.com/ai/vantas-ai-agent-wants-to-run-your-compliance-program-and-it-just-might ↩

17. https://softwarefinder.com/cybersecurity/vanta ↩

18. https://secureleap.tech/blog/vanta-review-pricing-top-alternatives-for-compliance-automation ↩

19. https://sprinto.com/blog/vanta-vs-secureframe-vs-laika/ ↩

20. https://www.vanta.com ↩

21. https://ithq.pro/cyber-services/secureframe-automated-compliance ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷

22. https://www.planetcompliance.com/compliance-software/drata-compliance-management/ ↩ ↩² ↩³ ↩⁴ ↩⁵

23. https://sprinto.com/blog/hyperproof-review/ ↩ ↩² ↩³

24. https://www.vanta.com/products/automated-compliance ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸

25. https://www.vanta.com/features ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

26. https://www.vanta.com/resources/300-integrations ↩ ↩² ↩³ ↩⁴ ↩⁵

27. https://secureframe.com/hub/soc-2/manual-vs-automated ↩ ↩²

28. https://ciobulletin.com/magazine/profile/drata-compliance-automation ↩ ↩² ↩³ ↩⁴ ↩⁵

29. https://www.planetcompliance.com/grc/hyperproof-risk-management/ ↩ ↩²

30. https://hyperproof.io ↩ ↩² ↩³ ↩⁴ ↩⁵

31. https://www.vanta.com/products/additional-frameworks ↩ ↩²

32. https://www.vanta.com/all-categories/compliance-frameworks ↩ ↩²

33. https://www.cyberarrow.io/blog/what-is-secureframe/ ↩

34. https://softwarefinder.com/cybersecurity/secureframe ↩

35. https://www.complyjet.com/blog/hyperproof-reviews ↩ ↩² ↩³

36. https://venturebeat.com/ai/vantas-ai-agent-wants-to-run-your-compliance-program-and-it-just-might ↩

37. https://secureframe.com ↩

38. https://www.g2.com/products/drata/features ↩

39. https://www.vanta.com/resources/best-security-questionnaire-automation-software ↩

40. https://secureframe.com/products/secureframe-comply ↩

41. https://www.vanta.com/resources/soc-2-compliance-for-healthcare ↩

42. https://www.vanta.com/collection/hipaa/hipaa-and-soc-2 ↩

43. https://www.vanta.com/resources/best-compliance-management-software ↩ ↩²

44. https://www.vanta.com/integrations ↩ ↩² ↩³

45. https://help.vanta.com/en/collections/12575285-integrations ↩ ↩² ↩³

46. https://secureframe.com/blog/compliance-automation ↩

47. https://secureframe.com/comply ↩

48. https://www.youtube.com/watch?v=bV7Eq4seXwM ↩

49. https://hyperproof.io/product/ ↩

50. https://hyperproof.io/resource/grc-platforms-features-you-need/ ↩

51. https://hyperproof.io/resource/hyperproof-top-rated-compliance-platform-2025/ ↩

52. https://hyperproof.io/compliance-operations/ ↩

53. https://www.smartsuite.com/blog/drata-vs-vanta-vs-smartsuite ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹

54. https://www.brightdefense.com/resources/best-soc-2-compliance-software/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

55. https://www.planetcompliance.com/compliance-software/drata-compliance-management/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹

56. https://hyperproof.io ↩

57. https://www.vanta.com/products/automated-compliance ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷

58. https://www.vanta.com/features ↩ ↩² ↩³ ↩⁴

59. https://ciobulletin.com/magazine/profile/drata-compliance-automation ↩ ↩² ↩³ ↩⁴ ↩⁵

60. https://www.vanta.com/integrations ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷

61. https://www.vanta.com/resources/300-integrations ↩ ↩² ↩³ ↩⁴

62. https://www.g2.com/products/drata/features ↩ ↩² ↩³

63. https://www.vanta.com/resources/best-security-questionnaire-automation-software ↩

64. https://www.vanta.com/products/access-reviews ↩ ↩²

65. https://www.vanta.com/resources/how-to-perform-effective-user-access-reviews ↩ ↩² ↩³

66. https://help.vanta.com/en/articles/11345425-access-reviews-system-reviewer-role ↩ ↩²

67. https://www.vanta.com/resources/best-compliance-management-software ↩ ↩²

68. https://gbhackers.com/best-cybersecurity-compliance-management-software/ ↩ ↩² ↩³ ↩⁴

69. https://ithq.pro/cyber-services/secureframe-automated-compliance ↩ ↩²

70. https://secureframe.com/products/secureframe-comply ↩

71. https://www.cyberarrow.io/blog/what-is-secureframe/ ↩

72. https://secureframe.com/hub/soc-2/manual-vs-automated ↩

73. https://www.complyjet.com/blog/vanta-reviews ↩ ↩²

74. https://help.vanta.com/en/collections/12575285-integrations ↩

75. https://hyperproof.io/resource/5-manual-tasks-compliance-software-automates/ ↩ ↩² ↩³

76. https://www.vanta.com/resources/how-do-you-perform-quarterly-access-reviews ↩

77. https://hyperproof.io/resource/effective-user-access-review-program/ ↩

78. https://docs.hyperproof.io/cm/en/access-reviews-main/ar-conduct-review.html ↩

79. https://hyperproof.io/resource/user-access-reviews/ ↩

80. https://hyperproof.io/product/comply/ ↩

81. https://docs.hyperproof.io/cm/en/access-reviews-main/ar-conduct-review/ar-reviewing ↩

82. https://hyperproof.io/resource/automated-controls-testing-release/ ↩

83. https://docs.hyperproof.io/admin/en/access-reviews-main/ar-process ↩

84. https://www.youtube.com/watch?v=NNg4vKYO_Ps ↩

85. https://trycomp.ai/drata-alternatives ↩

86. https://docs.hyperproof.io/cm/en/access-reviews-main/ar-setup ↩

87. https://www.nucamp.co/blog/solo-ai-tech-entrepreneur-2025-top-10-compliance-management-tools-for-ai-startups-in-2025 ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰ ↩¹¹ ↩¹² ↩¹³ ↩¹⁴ ↩¹⁵ ↩¹⁶ ↩¹⁷

88. https://wjarr.com/sites/default/files/WJARR-2024-2778.pdf ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰ ↩¹¹ ↩¹² ↩¹³ ↩¹⁴ ↩¹⁵ ↩¹⁶

89. https://cloudsecurityalliance.org/articles/how-genai-is-reshaping-grc-from-checklists-to-agentic-risk-intelligence ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰ ↩¹¹ ↩¹² ↩¹³ ↩¹⁴ ↩¹⁵ ↩¹⁶ ↩¹⁷

90. https://www.vanta.com/resources/best-compliance-management-software ↩ ↩²

91. https://www.vanta.com/resources/best-compliance-audit-software ↩ ↩²

92. https://avasant.com/report/governance-risk-and-compliance-services-leveraging-gen-ai-to-transform-grc-processes/ ↩ ↩² ↩³

93. https://www.strikegraph.com/blog/ai-grc ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹

94. https://hyperproof.io/guide-to-ai-risk-management-frameworks/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

95. https://www.anecdotes.ai/learn/grc-for-healthcare-challenges-technologies-and-best-practices ↩ ↩² ↩³

96. https://www.qtec-group.com/en/regulatory-requirements-ai-based-medical-devices/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰ ↩¹¹

97. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device ↩ ↩² ↩³ ↩⁴

98. https://bipartisanpolicy.org/issue-brief/fda-oversight-understanding-the-regulation-of-health-ai-tools/ ↩ ↩² ↩³ ↩⁴

99. https://www.vanta.com/resources/ai-compliance ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰

100. https://www.smartsuite.com/blog/vanta-pricing ↩

101. https://www.centraleyes.com/top-ai-compliance-tools/ ↩ ↩² ↩³

102. https://lumenalta.com/insights/8-compliance-automation-tools-in-2025 ↩ ↩²

103. https://clickup.com/blog/ai-assistant-for-compliance-and-audit/ ↩ ↩² ↩³ ↩⁴ ↩⁵

104. https://safebase.io/blog/ai-agent-for-vendor-risk-management ↩ ↩² ↩³ ↩⁴ ↩⁵

105. https://www.vanta.com/resources/how-vanta-ai-agent-transforms-compliance-work ↩ ↩²

106. https://www.vanta.com/resources/new-in-vanta-july-2025 ↩

107. https://trustible.ai/post/healthcare-regulation-of-ai-a-comprehensive-overview/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰ ↩¹¹ ↩¹² ↩¹³ ↩¹⁴

108. https://www.iso.org/standard/42001 ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹

109. https://regscale.com/resource-center/continuous-compliance-automation/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰

110. https://www.anecdotes.ai/learn/continuous-compliance-8-core-components-and-critical-best-practices ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹

111. https://cloudsecurityalliance.org/articles/how-to-transform-your-grc-with-continuous-controls-monitoring ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹

112. https://wjarr.com/sites/default/files/WJARR-2024-2778.pdf ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸

113. https://cloudsecurityalliance.org/articles/how-genai-is-reshaping-grc-from-checklists-to-agentic-risk-intelligence ↩ ↩² ↩³

114. https://www.nucamp.co/blog/solo-ai-tech-entrepreneur-2025-top-10-compliance-management-tools-for-ai-startups-in-2025 ↩ ↩² ↩³ ↩⁴ ↩⁵

115. https://www.vanta.com/resources/ai-compliance ↩ ↩² ↩³

116. https://hyperproof.io/guide-to-ai-risk-management-frameworks/ ↩ ↩² ↩³ ↩⁴ ↩⁵

117. https://www.a-lign.com/articles/preparing-for-eu-ai-act-compliance ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰ ↩¹¹ ↩¹²

118. https://pmc.ncbi.nlm.nih.gov/articles/PMC11379845/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰ ↩¹¹ ↩¹² ↩¹³ ↩¹⁴ ↩¹⁵ ↩¹⁶

119. https://www.scrut.io/post/ai-for-continuous-compliance-in-grc ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰

120. https://www.complyance.com/resources/why-continuous-compliance-monitoring-is-the-future-of-grc ↩ ↩²

121. https://pathlock.com/blog/grc/list-of-top-grc-tools-and-softwares/ ↩ ↩² ↩³ ↩⁴ ↩⁵

122. https://www.qtec-group.com/en/regulatory-requirements-ai-based-medical-devices/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷

123. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device ↩ ↩² ↩³ ↩⁴

124. https://intuitionlabs.ai/articles/ai-biopharma-regulation-2025 ↩ ↩² ↩³

125. https://boomi.com/blog/agentic-ai-compliance/ ↩ ↩² ↩³

126. https://www.linkedin.com/posts/scybers_ai-governance-compliance-activity-7380819906675527680-MHyc ↩ ↩² ↩³

127. https://www.centraleyes.com/top-ai-compliance-tools/ ↩ ↩²

128. https://www.anecdotes.ai/learn/grc-for-healthcare-challenges-technologies-and-best-practices ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷

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130. https://www.smartsuite.com/blog/360factors-pricing ↩ ↩²

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