PMI-CPMAI stresses that AI projects must define clear, measurable success criteria that are directly aligned with the problem the AI is intended to solve. In a network security context, the AI solution is being developed to “enhance network security,” which, in operational terms, translates to outcomes like faster incident response and better detection of threats and anomalies.

PMI’s guidance on benefits realization and performance management recommends using key performance indicators (KPIs) that are specific, measurable, and time-bound. For security, relevant KPIs typically include metrics such as mean time to detect (MTTD), mean time to respond (MTTR), detection rates, false positive/false negative rates, number of incidents contained, and reduction in successful breaches. By defining success criteria in terms of incident response times and threat detection rates, the project manager ties the AI system’s performance directly to business and operational outcomes, making it easier to monitor effectiveness and justify investment.

Implementing ML algorithms (option A) is a technical activity, not a definition of success. SWOT analysis and cost-benefit analysis (options C and D) can inform strategy and justification, but they do not, by themselves, define how success will be measured in day-to-day operations. PMI-CPMAI emphasizes metrics-driven evaluation, so using KPIs for incident response times and threat detection rates (option B) is the correct approach.