CLIENT STORY

A large federal agency needed to modernize core governance, risk, and compliance (GRC) operations in real time and within resource constraints.

PROBLEM

The agency maintained its security and privacy posture through an enterprise GRC platform and a tailored control baseline derived from NIST SP 800-53 rev 5.2. Beyond growing reporting and integration needs, keeping this ecosystem current, accurate, and well-documented was labor intensive. Three distinct bottlenecks existed:

1. Due to the size of the control catalog, a manual, line-by-line comparison across all three sources required two specialists working two weeks (roughly 20 person-days), with a high risk of transcription and omission errors across nearly 1,200 controls.
2. Reporting requirements evolved faster than existing development cycles allowed. Under traditional methods, custom dashboard development involved prototype sharing with stakeholders and updates based on their feedback. Manual development required three platform specialists, each expert in one of the contributing source systems, collaborating over 17 business days (roughly 3.5 calendar weeks), for a total of about 140 hours.
3. The internal teams responsible for integration and API development depended on a few subject-matter experts scattered across the agency for answers to configuration questions. One question typically required a SME to cross-reference five separate documents and perform hands-on examination inside the application, equating to some two hours of expert time.

The agency looked to Electrosoft to solve these issues.

SOLUTION

Recognizing that well-defined, high-volume, knowledge-intensive tasks offer prime opportunities to apply AI solutions, Electrosoft sought to leverage AI’s value responsibly without diminishing the role of the agency’s skilled staff. We developed and implemented an

1. AI-Assisted Control Set Reconciliation Application to expedite (a) the comparison between NIST SP 800-53 rev 5.2 and the tailored control baseline and (b) the production of the GRC platform import sheet, which compares the validated baseline against the import file to produce a corrected, import-ready version.
2. AI-Assisted Dashboard Generation Application to allow interactive dashboard creation directly from data exported from two enterprise systems. AI-generated dashboards enabled on-demand adjustments as users refined their requirements, eliminating lengthy redevelopment cycles.
3. AI Knowledge Assistant Application to consolidate platform knowledge within a single, in-depth question-and-answer resource, enabling team members to ask detailed configuration questions and receive relevant, contextual answers on demand.

RESULTS/BENEFITS

The AI applications delivered practical, measurable benefits in updating compliance controls, prototyping dashboards, and managing configuration knowledge, providing a foundation for responsible AI adoption in more GRC operations. Specifically, the AI-assisted applications delivered a

1. ~90 percent reduction in time to reconcile a security control catalog of nearly 1,200 controls across three independent sources from 20 days to 2, while detecting and correcting errors that likely would have been missed otherwise.
2. ~70 percent reduction in dashboard prototyping, delivering dashboards in one week rather than five months and allowing on-demand revisions in minutes rather than days.
3. ~95 percent reduction in obtaining answers to configuration questions, speeding responses to minutes rather than a day and easing reliance on a small pool of SMEs.

Electrosoft’s measurable benefits provide a foundation for broader, responsible AI adoption in this agency’s GRC operations. Three points relevant to future scaling of this approach include:

1. Human validation remains essential. In each case, AI accelerated the work of data reconciliation and error identification. However, people reviewed, confirmed, and acted on this output before correction.
2. Repeatability compounds returns. The reconciliation method in particular converts a one-time effort into a reusable process, so the savings recur with each future revision of the standard.
3. Governance of AI tooling. Future expansions should align with organizational policy on AI usage and the handling of sensitive information, particularly where external services are involved.