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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteNot by default. In a safety-critical device, AI-generated code is an untrusted draft. It can be used only when the organization proves that the resulting software meets defined requirements, controls hazards, passes independent verification and validation, and remains traceable, reproducible, secure, and maintainable throughout its regulated lifecycle.
Trust therefore belongs to the evidence and engineering controls around the code—not to the fact that a model produced it. No authoritative source establishes a universal defect rate or a percentage of AI code that can be trusted.
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What “trusted” means for safety-critical software
For ordinary applications, a developer may accept generated code after a code review and some automated tests. A device whose failure could injure a patient, crash an aircraft, disable a vehicle safety function, or create another hazardous condition requires a much stronger definition of trust.
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- Safety and functional requirements were defined before implementation and are complete, testable, and approved.
- Hazards were identified, risk controls were selected, and the software implements those controls correctly.
- Every requirement and risk control can be traced forward to design elements, source code, tests, anomalies, and the released build.
- Verification and validation were performed with methods proportionate to the device risk, including independent review where required.
- The exact model, prompts or input data, toolchain, dependencies, configuration, and generated output can be identified and reproduced.
- Cybersecurity, licensing, data handling, and software-supply-chain risks were assessed.
- Changes after release are controlled, monitored, validated, and reversible.
AI assistance does not remove any of these obligations. It adds another source of variability that must be controlled.
Why generated code starts as an untrusted artifact
Statistical generation is not engineering judgment
A generative model predicts likely text from patterns in its training and prompt context. It does not possess a validated understanding of your hardware timing, hazard analysis, electrical limits, sensor failure modes, real-time scheduling, or regulatory commitments. Code can look idiomatic and still violate a safety requirement, mishandle an integer boundary, introduce a race, or fail only under a rare fault combination.
Compilers and tests cannot prove the whole system safe
A successful build proves that the code is syntactically and semantically acceptable to a compiler. Unit tests demonstrate behavior for the cases they exercise. Neither result proves that requirements are complete, that untested boundary conditions are safe, or that the device behaves correctly when sensors, networks, power, memory, or timing behave abnormally.
Generated tests can share the same blind spots
When a model writes both implementation and tests, the tests may encode the same mistaken assumptions. Treat generated tests as proposed test artifacts. Review their independence, add tests derived directly from hazards and requirements, and use test design techniques that do not depend on the model’s interpretation of its own output.
How current standards and regulators apply
Medical devices: IEC 62304 and FDA oversight
The FDA’s recognized-consensus listing describes IEC 62304:2006+A1:2015 as defining lifecycle requirements for medical-device software. The standard applies when software is itself a medical device and when software is embedded in or integral to a finished device. It addresses planning, requirements, architecture, implementation, verification, maintenance, risk management interfaces, configuration management, and problem resolution.
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AI-generated source does not create an exemption from that lifecycle. The manufacturer remains responsible for the released software and its safety classification, regardless of whether code was typed by an employee, generated by a model, or obtained from a third party.
The FDA’s February 2026 Computer Software Assurance guidance says it provides recommendations for computer software assurance for computers and automated data-processing systems used in medical-device production or a quality-management system. Its risk-based approach supports applying more rigor where a software failure could have greater impact. A low-risk production-support script and software that controls therapy cannot be justified with the same evidence.
The FDA also states that it regulates medical devices, including AI-enabled medical devices, rather than regulating “AI” as a standalone category. The device’s intended use, controls, performance, and evidence determine the regulatory obligation.
Planned AI changes and the FDA lifecycle view
The FDA’s December 2024 final guidance on a Predetermined Change Control Plan (PCCP) addresses planned changes to AI-enabled device software functions and the information recommended in a marketing submission. A PCCP can describe anticipated modifications, the methodology for making them, and the boundaries within which they may be implemented. It does not authorize uncontrolled updates or eliminate validation.
A January 2025 FDA draft guidance proposed lifecycle-management and marketing-submission recommendations for AI-enabled devices. That document was explicitly a draft and was not for implementation at the time of publication. Teams should distinguish it from final requirements and confirm the current status of any guidance before relying on it.
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FDA’s AI-device resources describe lifecycle management as spanning development and validation, deployment, monitoring, maintenance, and modification. For an AI-assisted coding process, that means controls must cover the development tool and the delivered device, not only the initial code review.
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Automotive, aerospace, and other sectors
Automotive programs commonly map software development and safety evidence to ISO 26262, including hazard analysis, safety goals, technical safety concepts, software unit implementation, verification, and confirmation measures. The standard does not certify “AI-written code”; the vehicle manufacturer and suppliers must demonstrate that the item meets its assigned automotive safety integrity level and other applicable requirements.
Aerospace software is commonly developed under DO-178C (or the applicable national or program-specific framework). Evidence centers on plans, requirements, design, code, verification objectives, configuration management, quality assurance, and independence. A model’s output is simply another implementation input that must satisfy those objectives.
Industrial controls, rail, nuclear, robotics, and other domains add their own standards and authority expectations. The governing principle is consistent: certification attaches to a controlled development process and objective evidence, not to a tool’s marketing label.
NIST guidance for generative-AI development
NIST published SP 800-218A on July 26, 2024. It is an SSDF community profile for secure software development involving generative AI and dual-use foundation models. It adapts secure-development practices to risks such as model and data provenance, prompt manipulation, generated vulnerabilities, dependency integrity, and weaknesses introduced through AI-assisted workflows. It complements, rather than replaces, the safety standard and certification regime governing a particular device.
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A defensible workflow for using AI during development
The following sequence keeps safety decisions ahead of code generation and creates records an auditor or certification authority can inspect.
- Define intended use, safety classification, and hazards. Establish what the device does, foreseeable misuse, hazardous situations, risk controls, performance limits, and applicable jurisdictional requirements. Do not ask a model to infer these from an informal prompt.
- Write approved, testable requirements. Include timing, state behavior, error handling, resource limits, interfaces, diagnostics, and cybersecurity constraints. Give each requirement a stable identifier and acceptance criteria.
- Set an AI-use policy for the project. Specify which code, tests, documentation, or models may be generated; which data may enter a service; who approves use; and when AI assistance is prohibited, such as for unreviewed safety mechanisms or regulated records.
- Generate in a controlled environment. Record model name and version, system instructions, prompts, context files, output, date, user, tool versions, and configuration. Prevent confidential design data from entering an unauthorized service.
- Review architecture and code independently. A qualified engineer should examine interfaces, concurrency, numerical behavior, failure handling, resource use, and compliance with coding standards. The generating model must not be the sole reviewer or judge of correctness.
- Verify from requirements and hazards. Use static analysis, unit and integration tests, system tests, boundary-value testing, fault injection, timing and resource analysis, and security testing appropriate to the risk class. Add formal methods or model checking where the safety case requires them.
- Resolve anomalies before release. Record each defect, its safety impact, disposition, retest result, and approval. An accepted anomaly needs a documented rationale and risk assessment, not merely a comment in source code.
- Baseline and release reproducibly. Freeze source, generated artifacts, dependencies, build scripts, tool versions, configuration, test results, and approvals. A later engineer should be able to recreate the released binary or explain any controlled difference.
Verification depth should match risk
| Software situation | Evidence expected | Why AI use needs extra control |
|---|---|---|
| Non-safety production utility | Requirements, code review, automated tests, dependency and security checks | Generated defects may still affect quality or security, but the hazard analysis may permit lighter assurance |
| Safety-related embedded function | Bidirectional traceability, independent review, structural coverage, fault and boundary testing, configuration control | Short or plausible code can hide timing, state, or failure-mode defects |
| Highest-consequence control or protection function | Rigorous independence, analysis of defensive behavior, formal or semi-formal techniques where required, complete audit package | Model output cannot substitute for the additional assurance demanded by the safety case or certification authority |
The exact activities depend on the device’s classification, architecture, and authority. No single test percentage or model score establishes safety for every product.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Security, provenance, and supply-chain controls
AI-assisted development creates security questions that ordinary code review can miss:
- Dependencies: Identify every package, version, transitive dependency, license, known vulnerability, and update source. Generated code often imports libraries without explaining their maintenance or security status.
- Secrets and sensitive data: Keep credentials, patient data, proprietary schematics, and incident details out of prompts unless the service and contract explicitly authorize that handling.
- Prompt and model integrity: Protect system prompts, retrieval sources, and model endpoints from tampering. A changed model can produce materially different code from the same request.
- Reproducibility: Preserve hashes or equivalent identifiers for generated files, model versions, toolchains, and build inputs. “The model usually writes this” is not provenance.
- Adversarial content: Treat retrieved documentation, example code, and issue text as untrusted input. Prompt injection can cause a tool to ignore constraints or insert insecure changes.
- License compliance: Review generated snippets and imported code for license obligations and compatibility with the product’s distribution and regulatory records.
Managing updates after deployment
A safe initial release can become unsafe through an uncontrolled update. Establish change categories before shipment: defect fixes, security patches, parameter changes, model changes, retraining, and new functionality may require different assessments.
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For every change, determine whether it affects a hazard control, safety requirement, intended use, performance claim, or validated operating envelope. Re-run the affected verification and regression tests, update traceability and risk files, obtain required approvals, and retain the previous version for rollback. Monitor field behavior, alerts, drift, anomalies, and cybersecurity events. Define who can stop deployment when evidence is incomplete.
If an AI-enabled function is expected to change over time, a documented PCCP or equivalent change-control mechanism can define the permitted scope and evidence in advance. It does not permit changes outside that scope without a new assessment or submission.
How to evaluate an AI-assisted development approach
| Evaluation axis | Questions to ask |
|---|---|
| Requirement traceability | Can each generated change be linked to an approved requirement and hazard control? |
| Risk coverage | Are hazards, misuse cases, boundary conditions, and failure modes represented in the design and tests? |
| Verification independence | Is review performed by people and tools independent of the generation step? |
| Analysis depth | Are static analysis, coverage, fault injection, timing analysis, security tests, and formal methods used when warranted? |
| Provenance and reproducibility | Can the organization identify the model, prompt, context, toolchain, dependencies, and exact output? |
| Change discipline | Are model updates, prompt edits, regenerated code, and post-release changes controlled like other configuration items? |
| Regulatory fit | Does the evidence map to IEC 62304, ISO 26262, DO-178C, FDA expectations, or the applicable sector framework? |
| Audit cost | Can the team produce a complete, reviewable record without relying on undocumented human memory or a disappearing service history? |
Common failure modes and corrective actions
“The code passed the compiler, so it is safe.”
Problem: Compilation addresses language and build rules, not hazards or intended behavior.
Correction: Trace implementation to requirements and execute risk-based verification, including abnormal and fault conditions.
“The model wrote the tests, and all tests pass.”
Problem: Tests may repeat the model’s assumptions and omit unanticipated states.
Correction: Derive tests from approved requirements and hazard analysis; use independent review, mutation or property-based techniques where useful.
“We cannot reproduce the exact output, but the prompt is saved.”
Problem: Models, system prompts, retrieval context, and dependencies change.
Correction: Version and archive the model or service identifier, prompts, context, outputs, toolchain, and build inputs, or prohibit use where reproducibility cannot be demonstrated.
“A vendor says its model is safe for medical or automotive code.”
Problem: A tool claim is not device-level certification or evidence of your implementation’s safety.
Correction: Evaluate the tool as part of your quality and supply-chain process, then generate product-specific evidence under the applicable standard.
“We will validate after deployment because the model can learn.”
Problem: Uncontrolled learning or code changes can alter a validated safety function.
Correction: Freeze or tightly govern deployed behavior, define monitoring and rollback, and use a documented change-control plan for any permitted update.
What an audit-ready evidence package contains
A reviewer should be able to follow one uninterrupted chain from the device’s intended use to the released binary. The package commonly includes:
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- Requirements, architecture, interface definitions, hazard analysis, risk controls, and safety classification.
- AI-use records: approved policy, model and tool versions, prompts or generation records, review history, and provenance.
- Source, generated artifacts, dependencies, licenses, build instructions, configuration baselines, and release hashes.
- Static-analysis reports, code-review records, unit/integration/system results, structural coverage, fault-injection evidence, security analysis, and unresolved-anomaly assessments.
- Verification and validation summaries, change-impact analyses, approvals, and post-market monitoring and rollback procedures.
If the organization cannot produce this chain, it cannot credibly claim that generated code is trusted for a safety-critical device, regardless of how readable or sophisticated the code appears.
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