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Enterprise AI software does not replace the foundations of enterprise software: security, privacy, reliable integration, testing, and accountable operation still matter. What changes is the work needed to manage data and model behavior over time. Depending on the system, teams must also account for drift, harder-to-reproduce results, model-specific attacks, and new governance questions.
What stays the same when a company adopts AI software?
AI-enabled applications still need the controls expected of enterprise software. Organizations must manage access, protect sensitive information, integrate systems dependably, assess risk, and assign responsibility for decisions and operations. Established security and privacy frameworks remain useful starting points; NIST says AI risk management builds on them while addressing additional risks. NIST AI RMF 1.0, Appendix B (2023) explains the distinction.
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That means AI is an additional engineering and governance burden, not a reason to discard secure development or ordinary software lifecycle practices. Integration, policy compliance, technical capacity, and operational ownership remain familiar enterprise concerns.
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Data can shape behavior in production
In conventional software, teams primarily specify behavior through code and configuration. AI systems can also depend on data used to train, tune, ground, or operate them. If that data is stale, unrepresentative, poor quality, or mismatched to the context, system outputs may be unreliable even when the surrounding application code has not changed. Some tasks also lack a clear ground truth, making it harder to tell whether an answer is correct.
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NIST identifies data quality, context, representation, staleness, training changes, and drift among the issues that can increase AI risk. The practical implication is to monitor both the software and the data/model conditions that influence its behavior.
Behavior may be harder to predict and reproduce
Traditional code often produces the same result for the same inputs and version, though real enterprise systems can still be complex. AI systems may be more opaque, and reproducing an observed result can be difficult. Updates to data, prompts, models, or service providers may also change performance. These characteristics vary by system; they should not be assumed to affect every AI application equally.
For generative AI in particular, an answer can sound plausible without being accurate. Teams should define acceptable outcomes and escalation paths for the system’s intended use, rather than treating fluent output as proof of correctness.
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Testing has to address more than code paths
Software testing remains necessary, but AI testing can be harder to scope: teams may not know in advance every relevant input, failure mode, or expected answer. NIST identifies increased opacity and reproducibility concerns, emergent failure modes, underdeveloped testing standards, and difficulty deciding what to test.
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Testing should therefore reflect the system’s purpose and consequences. A low-impact internal assistant and an AI system that influences consequential decisions do not warrant identical evaluation. Teams should test realistic inputs, edge cases, known risks, and changes to models or data, and retain enough version and evaluation information to investigate regressions.
What changes in development and maintenance?
AI belongs inside the software development lifecycle, with added attention to model development and change. NIST Special Publication 800-218A supplements the Secure Software Development Framework (SSDF) version 1.1 with recommendations and tasks for generative AI and dual-use foundation model development. It is intended for model producers, AI system producers, and acquirers. It is guidance, not a certification or guarantee of safety. NIST SP 800-218A was published in July 2024.
Maintenance may need to respond to data, model, or concept drift—not only conventional defects or infrastructure changes. NIST’s 2023 AI RMF excerpt notes that AI systems may need more frequent maintenance and triggers for corrective maintenance when drift occurs. A practical lifecycle should identify who monitors behavior, what conditions prompt investigation, who can approve a model or data change, and how to roll back or limit a change when performance degrades.
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Keep established accountability, privacy, security, and risk-management processes, then extend them to cover the system’s data and model lifecycle. Depending on the use case, that can include review of bias, generative AI risks, third-party models, model-specific attacks, and how updates are evaluated and approved. NIST’s AI RMF Appendix B describes risks that may not be comprehensively addressed by earlier frameworks; it does not imply that every listed risk applies to every system.
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Third-party AI also makes procurement and oversight important. Buyers should establish what information may be sent to a provider, how dependencies and model changes are handled, what evaluations are available, and who is responsible for monitoring in operation. The appropriate answers depend on the contract, system design, data sensitivity, and consequences of failure.
What adoption evidence says—and what it does not
GAO’s July 29, 2025 report illustrates rapid change in a limited public-sector sample: inventories at 11 selected agencies reported 32 generative AI use cases in 2023 and 282 in 2024. Separately, officials at 10 of 12 selected agencies said existing federal policies, including data privacy policy, could present obstacles to adoption. These are findings about selected U.S. federal agencies, not representative estimates for private companies or a measure of AI’s productivity gains. GAO-25-107653
A practical decision checklist
- Define the use: Specify the task, users, data, degree of autonomy, and consequences of an incorrect result.
- Preserve core controls: Apply existing security, privacy, access, integration, and accountable-operation requirements.
- Map AI dependencies: Identify training or operational data, models, providers, versions, and the people responsible for each.
- Set evaluation criteria: Decide what acceptable performance means, which failure modes matter, and what evidence is needed before release.
- Plan for change: Monitor drift and model/data updates, define corrective triggers, and establish approval and rollback procedures.
- Review governance fit: Add controls for relevant bias, generative AI, third-party, and model-attack risks without assuming every risk applies to every system.
NIST notes that AI RMF 1.0 is being updated; the cited Appendix B is from the 2023 framework. Consult the current framework materials when using NIST’s guidance for a new program.
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