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Vendor-focused standards view

CSA AI Controls Matrix for Veeam / Securiti AI

See how Veeam / Securiti AI's public claims connect to security requirements and CSA AI Controls Matrix references.

What this page shows

Requirements connected to Veeam / Securiti AI's public claims

Only requirements with strong or limited public support appear. The framework references identify what to investigate; they do not establish implementation, conformance, certification, or product effectiveness.

Versionv1.1 (June 22, 2026)Current source
Related requirements11security questions in this research
Security requirements with public support8strong or limited public support
References63identifiers, clauses, safeguards, or categories

How to use this map

Framework connections help structure your evaluation

Each connection shows how a security requirement relates to this framework. Public vendor claims are shown separately, and deployed effectiveness still requires confirmation or testing.

Closely aligned
10
Contributes
1

Requirement connections

From Veeam / Securiti AI's public claims to questions to verify

Each row starts with a security requirement that has public support, then shows the connected framework references and the next question to verify.

Closely alignedSpecific reference

AI usage inventory

Maintain an inventory of AI tools, services, models, agents, software as a service (SaaS) AI capabilities, data flows, and provider relationships.

Framework references

DSP-03 · IAM-03 · STA-08 · UEM-04

Lifecycle

Govern · Identify · Monitor

Security requirements with public support

3 requirements with public support

Strong public supportUnapproved AI use discovery

Discover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.

Public claims reviewed
1
Next question to verify
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
Review source claims →
Strong public supportAI-feature discovery in business applications

Inventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.

Public claims reviewed
1
Next question to verify
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
Review source claims →
Strong public supportApproved AI usage monitoring

Monitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.

Public claims reviewed
1
Next question to verify
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Review source claims →
How this could be implemented

AI asset inventory · vendor/provider inventory · AI app discovery · model/application programming interface (API)/provider catalog

What public claims cannot prove

Public claims identify what to verify. They do not confirm control implementation or deployed effectiveness.

Why this connection is included

Use this for inventory/discovery claims. Do not infer control or blocking from visibility-only language. ISO 42001 excerpt: A.4.2 "identify and document relevant resources"; A.4.3 "data resources utilized"; A.4.4 "tooling resources utilized".

Closely alignedSpecific reference

AI usage monitoring

Monitor AI usage, user activity, prompts, responses, provider calls, runtime actions, and anomalous behavior.

Framework references

LOG-03 · LOG-07 · LOG-12 · LOG-14 · LOG-15 · LOG-16

Lifecycle

Monitor · Detect · Operate

Security requirements with public support

3 requirements with public support

Strong public supportUnapproved AI use discovery

Discover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.

Public claims reviewed
1
Next question to verify
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
Review source claims →
Strong public supportApproved AI usage monitoring

Monitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.

Public claims reviewed
1
Next question to verify
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Review source claims →
Limited public supportAction-taking agent monitoring

Observe and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.

Public claims reviewed
1
Next question to verify
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Review source claims →
How this could be implemented

prompt logs · user activity · provider telemetry · agent step tracing · tool call logging

What public claims cannot prove

Public claims identify what to verify. They do not confirm control implementation or deployed effectiveness.

Why this connection is included

Monitoring claims should identify what is monitored and where the telemetry comes from. ISO 42001 excerpt: 9.1 "what needs to be monitored"; A.6.2.6 "system and performance monitoring"; A.6.2.8 "event logs should be enabled".

Closely alignedSpecific reference

unapproved AI control

Enforce policy against unapproved AI tools or unsafe AI interactions through blocking, coaching, allowlists, or runtime controls.

Framework references

AIS-11 · GRC-09 · TVM-13 · UEM-02

Lifecycle

Protect · Deploy · Operate

Security requirements with public support

2 requirements with public support

Limited public supportControls for unapproved AI use

Block, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.

Public claims reviewed
1
Next question to verify
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
Review source claims →
Limited public supportGenerative AI application security

Protect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.

Public claims reviewed
1
Next question to verify
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
Review source claims →
How this could be implemented

blocking · allowlists · browser enforcement · policy coaching · large language model (LLM) firewall · tool allowlists

What public claims cannot prove

Public claims identify what to verify. They do not confirm control implementation or deployed effectiveness.

Why this connection is included

Distinguish hard blocking from warning, coaching, logging, or after-the-fact reporting. ISO 42001 excerpt: A.9.2 "processes for the responsible use"; A.9.3 "objectives to guide"; A.9.4 "intended uses".

Closely alignedSpecific reference

AI data protection

Prevent sensitive data exposure through prompts, responses, files, retrieval, memory, embeddings, or AI-connected workflows.

Framework references

CEK-03 · DSP-17 · DSP-20 · DSP-22 · IAM-16 · UEM-11

Lifecycle

Protect · Operate · Monitor

Security requirements with public support

3 requirements with public support

Strong public supportSensitive-data protection for generative AI

Detect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.

Public claims reviewed
1
Next question to verify
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
Review source claims →
Limited public supportGenerative AI application security

Protect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.

Public claims reviewed
1
Next question to verify
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
Review source claims →
Strong public supportApproved AI usage monitoring

Monitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.

Public claims reviewed
1
Next question to verify
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Review source claims →
How this could be implemented

data loss prevention (DLP) · redaction · sensitive data detection · output filtering · memory scoping · data-in-use protection

What public claims cannot prove

Public claims identify what to verify. They do not confirm control implementation or deployed effectiveness.

Why this connection is included

Require source language that ties data protection to AI use, not generic encryption alone. ISO 42001 excerpt: A.7.3 "acquisition and selection"; A.7.4 "requirements for data quality"; A.7.5 "recording the provenance"; A.7.6 "data preparation methods".

Closely alignedSpecific reference

generative AI application security

Secure large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, tool calls, application programming interfaces (APIs), model interactions, and runtime behavior.

Framework references

AIS-05 · AIS-08 · AIS-09 · AIS-10 · AIS-11 · AIS-13 · AIS-15 · TVM-07 · TVM-13

Lifecycle

Develop · Test · Release · Deploy · Operate

Security requirements with public support

3 requirements with public support

Limited public supportGenerative AI application security

Protect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.

Public claims reviewed
1
Next question to verify
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
Review source claims →
Strong public supportSensitive-data protection for generative AI

Detect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.

Public claims reviewed
1
Next question to verify
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
Review source claims →
Limited public supportControls for unapproved AI use

Block, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.

Public claims reviewed
1
Next question to verify
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
Review source claims →
How this could be implemented

prompt injection defense · large language model (LLM) app scanning · retrieval-augmented generation (RAG) security · guardrails · model/application interaction security

What public claims cannot prove

Public claims identify what to verify. They do not confirm control implementation or deployed effectiveness.

Why this connection is included

Use for large language model (LLM) application controls. Separate from employee AI usage governance when possible. ISO 42001 excerpt: A.6.2.4 "verification and validation measures"; A.6.2.5 "deployment plan"; A.6.2.6 "ongoing operation"; A.6.2.8 "event logs".

Closely alignedSpecific reference

AI governance, risk, and compliance operations

Maintain accountable AI inventory, policy, risk assessments, approvals, exceptions, regulatory mappings, third-party oversight, and audit evidence across the AI lifecycle.

Framework references

GRC-09 · STA-08 · LOG-16

Lifecycle

Govern · Identify · Assess · Approve · Monitor

Security requirements with public support

3 requirements with public support

Strong public supportAI governance, risk, and compliance

Inventory AI systems and owners, translate policy and regulatory obligations into governed workflows, assess risk, manage approvals and exceptions, and retain audit evidence across the AI lifecycle.

Public claims reviewed
1
Next question to verify
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
Review source claims →
Strong public supportApproved AI usage monitoring

Monitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.

Public claims reviewed
1
Next question to verify
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Review source claims →
Strong public supportAI-feature discovery in business applications

Inventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.

Public claims reviewed
1
Next question to verify
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
Review source claims →
How this could be implemented

AI system registry · risk tiering · policy workflow · regulatory mapping · approval and exception workflow · audit evidence

What public claims cannot prove

Public claims identify what to verify. They do not confirm control implementation or deployed effectiveness.

Why this connection is included

Governance evidence must show accountable workflow or decision evidence, not technical inventory alone. ISO 42001 excerpt: A.9.2 "processes for the responsible use"; A.9.3 "objectives to guide"; A.10.2 "allocated between".

Closely alignedSpecific reference

Third-party and software as a service (SaaS) AI risk

Understand and monitor AI risk introduced by external software as a service (SaaS), AI providers, embedded AI features, and supplier services.

Framework references

STA-01 · STA-08 · STA-09 · STA-10 · STA-13 · STA-16

Lifecycle

Govern · Identify · Detect

Security requirements with public support

2 requirements with public support

Strong public supportAI-feature discovery in business applications

Inventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.

Public claims reviewed
1
Next question to verify
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
Review source claims →
Strong public supportUnapproved AI use discovery

Discover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.

Public claims reviewed
1
Next question to verify
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
Review source claims →
How this could be implemented

third-party AI inventory · supplier AI service monitoring · software as a service (SaaS) AI detection · provider activity monitoring

What public claims cannot prove

Public claims identify what to verify. They do not confirm control implementation or deployed effectiveness.

Why this connection is included

Third-party sources are only allowed in the project if one source class covers at least 80 percent of vendors. ISO 42001 excerpt: A.10.2 "allocated between"; A.10.3 "provided by suppliers aligns".

Closely alignedSpecific reference

Agentic telemetry and behavior monitoring

Observe agent steps, plans, goals, memory, delegation, tool use, and anomalies during runtime.

Framework references

LOG-03 · LOG-07 · LOG-12 · LOG-14 · LOG-15 · LOG-16 · MDS-10 · SEF-06

Lifecycle

Operate · Monitor · Detect

Security requirements with public support

1 requirement with public support

Limited public supportAction-taking agent monitoring

Observe and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.

Public claims reviewed
1
Next question to verify
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Review source claims →
How this could be implemented

agent traces · goal drift detection · memory mutation monitoring · tool execution logs · anomalous delegation

What public claims cannot prove

Public claims identify what to verify. They do not confirm control implementation or deployed effectiveness.

Why this connection is included

Require explicit agent language. Generic chatbot monitoring is not enough. ISO 42001 excerpt: 9.1 "monitoring and measuring"; A.6.2.6 "system and performance monitoring"; A.6.2.8 "event logs should be enabled".

Closely alignedSpecific reference

Agent-to-agent and tool communication security

Secure trust, authorization, message flows, tool access, and communication between agents, tools, application programming interfaces (APIs), and external services.

Framework references

AIS-08 · AIS-11 · IAM-15 · IAM-18 · IPY-03 · LOG-12

Lifecycle

Identify · Protect · Deploy · Monitor

Security requirements with public support

2 requirements with public support

Limited public supportAction-taking agent monitoring

Observe and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.

Public claims reviewed
1
Next question to verify
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Review source claims →
Limited public supportGenerative AI application security

Protect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.

Public claims reviewed
1
Next question to verify
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
Review source claims →
How this could be implemented

agent-to-agent (A2A) registry · mutual TLS (mTLS) · inter-agent authorization · connector contracts · tool schemas · message logs

What public claims cannot prove

Public claims identify what to verify. They do not confirm control implementation or deployed effectiveness.

Why this connection is included

Do not map agent-to-agent (A2A) unless a source mentions agents, tools, connectors, protocols, or machine-to-machine authorization. ISO 42001 has no direct agent-to-agent (A2A) security control. ISO 42001 excerpt: A.6.2.4 "verification and validation measures"; A.6.2.8 "event logs"; A.10.2 "allocated between".

Closely alignedSpecific reference

non-human identity (NHI) and AI-agent identity governance

Manage identities, credentials, privileges, secrets, service accounts, and lifecycle for AI agents and other non-human identities.

Framework references

IAM-03 · IAM-05 · IAM-12 · IAM-14 · IAM-15 · IAM-18 · LOG-13

Lifecycle

Govern · Protect · Deploy · Operate

Security requirements with public support

1 requirement with public support

Strong public supportAI-feature discovery in business applications

Inventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.

Public claims reviewed
1
Next question to verify
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
Review source claims →
How this could be implemented

non-human identity (NHI) inventory · AI service accounts · least privilege · credential rotation · scoped tokens · privilege review

What public claims cannot prove

Public claims identify what to verify. They do not confirm control implementation or deployed effectiveness.

Why this connection is included

Separate AI-agent identity claims from generic human identity and access management (IAM) unless the vendor explicitly spans service accounts or NHIs. ISO 42001 has no direct non-human identity (NHI) identity control. ISO 42001 excerpt: A.4.2 "relevant resources"; A.10.2 "responsibilities".

ContributesSpecific reference

AI FinOps and cost accountability

Attribute AI usage and spend to accountable owners, workflows, agents, models, and business units while enforcing budget, rate-limit, and routing controls.

Framework references

BCR-02 · GRC-02 · I&S-02 · LOG-14

Lifecycle

Govern · Operate · Optimize

Security requirements with public support

2 requirements with public support

Strong public supportApproved AI usage monitoring

Monitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.

Public claims reviewed
1
Next question to verify
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Review source claims →
Limited public supportAction-taking agent monitoring

Observe and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.

Public claims reviewed
1
Next question to verify
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Review source claims →
How this could be implemented

AI spend attribution · runaway token burn detection · budget enforcement · rate limits · model routing · owner-based cost reporting

What public claims cannot prove

Public claims identify what to verify. They do not confirm control implementation or deployed effectiveness.

Why this connection is included

Keep this separate from licensing. Require source language about operational usage, spend, budgets, rate limits, model routing, or owner attribution. ISO 42001 excerpt: A.9.2 "responsible use of AI systems"; A.9.3 "objectives to guide".

This page organizes research. Audit conclusions, certification assessments, control implementation statements, and vendor endorsements require separate evidence.