2026 Pueo Team
Automating Cybersecurity Assessment Reporting for Classified Environments
At A Glance:
Capstone Sponsor: Pueo Business Solutions
Faculty Advisor: Dr. Joseph Simpson
Capstone Team Members: Daniel Belayneh, Kariss Gibbs, Ben Halambeck, James Martin
Solution Summary: Built an automated, fully offline pipeline that ingests cybersecurity assessment data, maps failed safeguards to control frameworks with risk-based scoring, and generates stakeholder-ready reports for Pueo's analyst teams, projected to return $185,000 in year one and $871,342 in net present value over five years.
The Challenge
Pueo Business Solutions is a government contractor that produces cybersecurity risk assessments for organizations in the defense sector of government, and its assessment volume has grown several-fold in recent years. Every report required dozens of manual hours across a large analyst team, and thousands of analyst hours were spent annually on documentation alone. Because no standardized pipeline existed, analysts started from scratch on every engagement, reconciling inconsistently formatted assessment workbooks by hand before failed findings could be mapped to internal controls and weighted by risk for decision makers. All of it had to happen inside classified networks where conventional deployment options were not available.
The Solution
The student team built an automated report generation pipeline organized around four objectives: ingest, map and score, generate, and deploy. The tool automates extraction of assessment data into a normalized data pipeline, links failed safeguards to control frameworks and applies risk-based scoring, and produces formatted, stakeholder-ready reports automatically. One constraint shaped every design decision: the system had to operate fully offline, with no administrative privileges, inside classified environments.
Work ran across six phases from October 2025 through June 2026, beginning with project initiation and requirements analysis, moving through a site visit and development, and closing with system validation and a final delivery and knowledge transfer. The students spent the fall learning how Pueo analysts actually work before writing code, then iterated on the build until it ran on multiple local machines. Because the sponsor's live data was classified, the team validated the pipeline against synthetic data and kept a human in the loop at each review point, adding a large language model component during a short spring development sprint.
The Impact
The team's economic analysis projects a $185,000 annual benefit in year one and a $225,000 annual benefit from year two onward, yielding an $871,342 net present value over five years. Beyond the dollars, Pueo gains a repeatable pipeline in place of ad hoc report writing. The team recommended a phased rollout that starts by validating the system against live data, then rolls out updated documentation and onboarding, pilots expanded outputs and trend analysis capabilities, and ultimately extends the automation organization-wide across other functions.
2026 Pueo Team Public Presentation