An approach to AI development that builds safety, security, and governance into the system before deployment. It assumes runtime behaviour must be controlled, not just evaluated after the fact, and that both model and operational risk need shared oversight.
Expanded Definition
secure by design AI is a development posture that treats AI safety, security, and governance as engineering requirements from the start, not as review items after a model is live. In practice, that means threat modeling prompts, tools, model outputs, data flows, and administrative controls before release, then hardening the deployment path so runtime behaviour is constrained by policy, access boundaries, and monitoring. The concept overlaps with secure software design, but it is broader because AI systems can inherit risks from training data, orchestration layers, embedded secrets, and agentic tool use. Standards are still evolving, so usage across vendors is not fully uniform; some teams use it to describe secure AI development, while others mean secure AI operations plus governance. For a baseline governance lens, see the EU Cyber Resilience Act and the NIST framing of secure lifecycle practice in the NIST Security and Privacy Controls. The most common misapplication is treating secure by design AI as a model-quality check, which occurs when teams validate outputs but do not secure identities, secrets, and tool permissions around the system.
Examples and Use Cases
Implementing Secure by Design AI rigorously often introduces slower release cycles and more cross-functional review, requiring organisations to weigh faster experimentation against stronger control over model behaviour and data exposure.
- A team designing an internal agent restricts tool access by role, so the agent can read approved documents but cannot call production systems without explicit approval.
- A model pipeline scans training and retrieval datasets for secrets before deployment, reducing the chance that sensitive credentials are memorised or reproduced. The risk is especially visible in NHIMG research such as the DeepSeek breach.
- Security reviewers require prompt-injection testing and output filtering before launch, rather than waiting for post-deployment abuse reports.
- Administrators separate model administration from business usage, applying zero standing privilege so operators only gain elevated access during a justified change window.
- Teams align AI governance with secure development guidance such as OWASP Top 10 for Large Language Model Applications, then map those risks into build-time controls and release gates.
In NHI programs, this term also covers the non-obvious problem of agent credentials, because an AI system that can act is only as secure as the identity and permission model behind it.
Why It Matters in NHI Security
Secure by Design AI matters because AI systems often become identity-rich attack surfaces long before they become obvious security incidents. A model may be well aligned in testing, yet still expose tokens, inherit overbroad entitlements, or propagate sensitive patterns into logs and downstream tools. NHIMG research shows that 43% of security professionals are concerned about AI systems learning and reproducing sensitive information patterns from codebases, which is a direct signal that model governance and secrets hygiene are converging risks. The same dynamic appears in attack pathways where exposed credentials are abused quickly, as described in LLMjacking: How Attackers Hijack AI Using Compromised NHIs. In a Secure by Design program, the goal is not only preventing unsafe outputs, but also preventing the AI from becoming a privileged intermediary that can be manipulated, impersonated, or expanded beyond its intended scope. Organisations typically encounter the consequences only after an agent leaks a secret, makes an unauthorized action, or is exploited through a compromised NHI, at which point the secure-by-design requirement becomes operationally unavoidable to address.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A2 | Agentic AI guidance addresses unsafe tool use, prompt injection, and runtime control. |
| OWASP Non-Human Identity Top 10 | NHI-02 | Secure by design depends on preventing secret exposure and unmanaged machine identities. |
| NIST AI RMF | AI RMF frames governable, secure AI lifecycle risk management across design and operations. |
Embed risk mapping, validation, and monitoring into the AI lifecycle from requirements onward.
