AICM AtlasCSA AI Controls Matrix
AIS · Application & Interface Security
AIS-06Cloud & AI Related

Secure Application Deployment

Specification

Establish and implement strategies and capabilities for secure, standardized, and compliant application deployment. Automate where possible.

Threat coverage

Model manipulation
Data poisoning
Sensitive data disclosure
Model theft
Model/Service Failure
Insecure supply chain
Insecure apps/plugins
Denial of Service
Loss of governance

Architectural relevance

Physical infrastructure
Network
Compute
Storage
Application
Data

Lifecycle

Preparation

Resource provisioning, Team and expertise

Development

Guardrails, Design

Evaluation

Validation/Red Teaming

Deployment

AI applications, Orchestration

Delivery

Maintenance, Continuous improvement, Operations, Continuous monitoring

Retirement

Not applicable

Ownership / SSRM

PI

Shared Cloud Service Provider-Model Provider (Shared CSP-MP)

The CSP and MP are jointly responsible and accountable for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer.

Model

Owned by the Model Provider (MP)

The model provider (MP) designs, develops, and implements the control as part of their services or products to mitigate security, privacy, or compliance risks associated with the Large Language Model (LLM). Model Providers are entities that develop, train, and distribute foundational and fine-tuned AI models for various applications. They create the underlying AI capabilities that other actors build upon. Model Providers are responsible for model architecture, training methodologies, performance characteristics, and documentation of capabilities and limitations. They operate at the foundation layer of the AI stack and may provide direct API access to their models. Examples: OpenAI (GPT, DALL-E, Whisper), Anthropic(Claude), Google(Gemini), Meta(Llama), as well as any customized model.

Orchestrated

Shared Model Provider-Orchestrated Service Provider (Shared MP-OSP)

The MP and OSP are jointly responsible and accountable for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer.

Application

Owned by the Application Provider (AP)

The Application Provider (AP) is responsible for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer. The AP is responsible and accountable for the implementation of the control within its own infrastructure/environment. If the control has downstream implications on the users/customers, the AP is responsible for enabling the customer and/or upstream partner in the implementation/configuration of the control within their risk management approach. The AP is accountable for carrying out the due diligence on its upstream providers (e.g MPs, Orchestrated Services) to verify that they implement the control as it relates to the service/product develop and offered by the AP. These providers build and offer end-user applications that leverage generative AI models for specific tasks such as content creation, chatbots, code generation, and enterprise automation. These applications are often delivered as software-as-a-service (SaaS) solutions. These providers focus on user interfaces, application logic, domain-specific functionality, and overall user experience rather than underlying model development. Example: OpenAI (GPTs,Assistants), Zapier, CustomGPT, Microsoft Copilot (integrated into Office products), Jasper (AI-driven content generation), Notion AI (AI-enhanced productivity tools), Adobe Firefly (AI-generated media), and AI-powered customer service solutions like Amazon Rufus, as well as any organization that develops its AI-based application internally.

Implementation guidelines

[Applicable to all providers (CSP, MP, OSP, AP) except AIC]
1. Define Deployment Strategy Scope: Establish provider-specific strategies for secure, standardized, and compliant application deployment as outlined in the provider-specific sections below. Ensure strategies cover the entire deployment lifecycle, including build, testing, staging, and production phases, with a focus on automation to reduce human error and ensure consistency.

2. Deployment Governance Structure: Establish clear roles and responsibilities for overseeing secure deployment processes, involving cross-functional teams (e.g., DevOps, security, compliance) and governance bodies. Define approval workflows that include senior management, security architects, and compliance officers to ensure deployments align with organizational security and regulatory requirements.

3. Deployment Documentation Standards: Define structured documentation standards for deployment processes, including configuration management, automation scripts, and compliance checklists. Use consistent templates to document deployment workflows, security controls, and rollback procedures to ensure traceability and auditability.

4. Deployment Management Framework: Implement a structured review and update process for deployment strategies. Conduct reviews at least annually or following significant changes (e.g., new deployment tools, infrastructure updates, or regulatory changes). Ensure alignment with relevant standards (e.g., NIST 800-53, ISO 27001, CIS Controls) and AI-specific frameworks like NIST AI Risk Management Framework. Automate policy enforcement where possible using CI/CD pipelines and configuration management tools.

5. Communication and Training Standards: Define requirements for communicating deployment strategies, including formal distribution of process documentation, mandatory training for DevOps and security teams, and awareness programs for stakeholders. Ensure deployment policies and automation tools are accessible via internal portals and that teams are trained on secure deployment practices and compliance requirements.

6. Quality Control and Automation Standards: Define policies for quality assurance of deployment processes, including automated security scanning, infrastructure-as-code (IaC) validation, and compliance checks within CI/CD pipelines. Implement automated testing (e.g., unit, integration, security tests) and monitoring to ensure deployments meet security and performance standards. Establish rollback mechanisms to address deployment failures securely.

Auditing guidelines

1. Review Secure Service Catalog and Templates: CSPs must offer “secure-by-default” building blocks (e.g., hardened images, encrypted storage). Examine infrastructure templates or service catalog entries for built-in security controls.

2. Inspect Customer-Facing Deployment APIs: Test APIs for rate-limiting, versioning, and authentication enforcement. Review security regression test results.

3. Evaluate Enforcement of Configuration Policies: Customers rely on CSP tools like config or policy-as-code to stay compliant. Review automated controls for compliance (e.g., CIS benchmarks) and check audit logs for remediation actions.

4. Check Third-Party Offering Vetting: AI software marketplaces or container registries may host vulnerable or unverified runtimes. Inspect the vetting process for containers, models, or functions published to customers.

5. Validate Disaster Recovery Readiness: CSP outages can impact thousands of AI systems and deployment continuity is essential. Request DR test records, review automated failover procedures, and examine data replication configurations.

From CCM:
1. Examine policy and procedures for implementation of application deployment.
2. Determine if segregation of duties (role and responsibilities) is clearly defined among security and application teams.
3. Determine if an identification and integration process is defined and verified for application deployment processes.
4. Evaluate the extent of automation deployed and the criteria used.

Standards mappings

ISO 42001Partial Gap
42001: 8.2
42001: 8.3
42001: 8.4
42001: B.6.2.5
42001: B.6.2.6
27001: A.8.9
27001: A.5.31
27001: A.5.32
27001: A.8.32
Addendum

Explicitly incorporate standardized deployment process elements, separation of environments, record‑keeping for compliance, and encouragement of automation.

EU AI ActNo Gap
Article 9 (6)
Article 9 (7)
Article 9 (8)
Article 26
Article 50
Article 53 and Annex XI
Article 55
Addendum

N/A

NIST AI 600-1No Gap
MEASURE 2.9
MEASURE 2.10
Addendum

N/A

BSI AIC4No Gap
DEV-01
DEV-03
DEV-08
DEV-09
PSS-02
PSS-05
PSS-09
PSS-10
Addendum

N/A

AI-CAIQ questions (1)

AIS-06.1

Are strategies and capabilities established and implemented for secure, standardized, and compliant application deployment?