Secure Application Deployment
Specification
Establish and implement strategies and capabilities for secure, standardized, and compliant application deployment. Automate where possible.
Threat coverage
Architectural relevance
Lifecycle
Resource provisioning, Team and expertise
Guardrails, Design
Validation/Red Teaming
AI applications, Orchestration
Maintenance, Continuous improvement, Operations, Continuous monitoring
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
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
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.
Article 9 (6) Article 9 (7) Article 9 (8) Article 26 Article 50 Article 53 and Annex XI Article 55
Addendum
N/A
MEASURE 2.9 MEASURE 2.10
Addendum
N/A
DEV-01 DEV-03 DEV-08 DEV-09 PSS-02 PSS-05 PSS-09 PSS-10
Addendum
N/A
AI-CAIQ questions (1)
Are strategies and capabilities established and implemented for secure, standardized, and compliant application deployment?