AI systems need secure-by-design foundations because they are software products—and they add risks that ordinary software security practices do not fully cover. That means protecting the familiar application, infrastructure and dependency layers while also assessing models, training data, model access and adversarial inputs throughout the system’s lifecycle. No framework or control set guarantees security; the case for secure design is that it reduces avoidable risk and makes security a product responsibility rather than a burden left to customers.
What does secure by design mean for an AI system?
Secure by design means treating security as a core requirement from product planning through development, deployment, maintenance and end of life. It is not a final review added after a model or product is built. CISA’s article Software Must Be Secure by Design, and Artificial Intelligence Is No Exception says AI systems should be secure by default, with security treated as a customer requirement across the product lifecycle.
That principle includes usable defaults. CISA’s AI Security Lead Christine Lai and Senior Technical Advisor Dr. Jonathan Spring wrote: “AI systems must be secure to use out of the box, with little to no configuration changes or additional cost.” Secure defaults matter because customers may not have the expertise, time or control to compensate for unsafe product choices.
Secure by design does not mean that a system cannot be compromised. It means the producer takes responsibility for reducing foreseeable risks, making security decisions explicit, and supporting customers when vulnerabilities or incidents occur.
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Why is AI security different from regular software security?
AI systems inherit ordinary software risks and introduce additional concerns involving models, data and behavior. NIST’s AI Research – Security and Resilience guidance says some cybersecurity risks are the same as in software generally, while other risks are distinct or expanded. It also notes that current guidance does not comprehensively cover all AI-related risks.
- Adversarial evasion: Inputs are crafted to make a model produce an incorrect or unsafe result.
- Model extraction: An attacker uses access to a model to infer or reproduce aspects of the model itself.
- Membership inference: An attacker tries to determine whether particular information was included in a model’s training data.
- Confidentiality, integrity and availability: AI systems still need protection against disclosure, unauthorized change and disruption, including attacks on the wider system around the model.
These risks do not replace familiar weaknesses such as exposed credentials, insecure interfaces or vulnerable dependencies. A model-specific safeguard cannot compensate for a flaw in a conventional software component that can compromise the system around it.
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What should teams secure across the AI lifecycle?
Apply established secure software engineering and operations practices to AI products, then add evaluations for model- and data-specific risks. A practical lifecycle checklist is:
- Plan and assess risk. Identify the system’s intended use, sensitive data, likely threats and security ownership before implementation. Revisit those assumptions when the model, data, integrations or deployment context changes.
- Develop securely. Use secure development practices for the full product, not only model code. Protect conventional components and dependencies as well as model-related assets.
- Track the supply chain. Maintain an inventory of models, dependencies and relevant data. CISA recommends capturing AI models and dependencies, including data, in software bills of materials (SBOMs).
- Test the whole system. Include ordinary software security testing and AI-specific evaluation. Assess adversarial inputs and threats to model confidentiality, integrity and availability; do not assume a model’s expected behavior under normal inputs establishes its security.
- Control access to sensitive assets. Treat model access and training data as sensitive. CISA discusses model inversion and data extraction and recommends restricting model access at a level comparable to access to training data.
- Prepare for operation and change. Define vulnerability handling, incident response and end-of-life plans. Monitor relevant components and respond when defects or risks are identified.
This is a product and organizational responsibility, not just a model-development task. CISA’s November 26, 2023 announcement of joint secure-AI development guidelines emphasizes ownership of customer security outcomes, transparency, accountability and organizational structures that prioritize secure design.
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Which NIST and CISA guidance applies?
These resources serve different purposes. They can inform a security program, but none should be read as a certification that an AI system is secure.
| Resource | What it contributes | Scope and status |
|---|---|---|
| CISA, Software Must Be Secure by Design, and Artificial Intelligence Is No Exception (August 18, 2023) | Applies secure-by-design principles to AI products, including lifecycle security, secure defaults, supply-chain visibility and protection of models and data. | Practical guidance; emphasizes producer responsibility and customer outcomes. |
| NIST SP 800-218A, Secure Software Development Practices for Generative AI and Dual-Use Foundation Models: An SSDF Community Profile | Adds AI-specific secure-development practices to the Secure Software Development Framework (SSDF). | Final publication dated July 26, 2024; intended for AI model and system producers and acquirers, and designed to be considered alongside NIST SP 800-218. |
| NIST AI Risk Management Framework (AI RMF) | Provides a framework for incorporating trustworthiness considerations into AI design, development, use and evaluation. | Voluntary. NIST’s page, accessed September 28, 2026, says AI RMF 1.0 is being revised. The page records the generative AI profile NIST-AI-600-1, released July 26, 2024, and a concept note for a trustworthy AI critical-infrastructure profile released April 7, 2026. |
| NIST AI Research – Security and Resilience | Describes AI security and resilience concerns, including evasion, model extraction, membership inference, availability and broader system attack surfaces. | Research and guidance context; NIST says the field is active and changing rapidly and that existing frameworks do not comprehensively address all these risks. |
Use the resources according to the work at hand: SP 800-218A is a secure-development profile, the AI RMF is a voluntary risk-management framework, and CISA’s article makes the secure-by-design case for AI products. The comparison is about role and scope, not a ranking of which one is “best.”
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Who is accountable for secure AI?
Security responsibilities are shared across the organizations that build, integrate, acquire and operate an AI system, but they are not interchangeable. Producers control many design and default choices; system integrators combine models with applications and other components; acquirers and operators make deployment and use decisions. CISA’s guidance puts particular emphasis on producers owning customer security outcomes rather than transferring the burden to users.
That accountability requires organizational support as well as technical controls: clear ownership, transparent communication, and processes for addressing vulnerabilities and incidents. A secure development checklist is unlikely to work if teams lack authority or resources to act on the risks they find.
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What secure by design can—and cannot—promise
Secure-by-design practices can make security a routine part of building and maintaining AI products, improve visibility into components and dependencies, and prompt evaluation of risks specific to models and data. They cannot eliminate all vulnerabilities or establish that a system is safe for every use. NIST describes AI security as a rapidly changing area, and its guidance identifies coverage gaps in existing frameworks.
There is no directly relevant, well-sourced statistic in the cited guidance establishing how prevalent insecure AI systems are or measuring the effectiveness of secure-by-design programs. The case for the approach rests instead on risk management: AI products remain exposed to ordinary software weaknesses, while their models and data introduce further risks that need deliberate treatment.
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