By Anand Trivedi, REI Systems’ AI Offering Lead and Narpender Bawa, Senior Director at REI Systems

Context: Reimagining Software Governance in the Federal Sector with AI

Across the federal government, software governance and risk management processes remain burdened by manual assessments, rigid documentation requirements, and siloed workflows. Gaining authorization to operate (ATO) for new or updated systems often takes months—an unsustainable pace in an era where mission needs evolve rapidly and agility is essential.

There is a growing imperative to modernize this outdated paradigm. Emerging technologies – particularly automation, artificial intelligence (AI), and continuous risk assessment – offer the potential to accelerate software delivery while maintaining rigorous security standards.

AI stands out as a transformative enabler in this space. With advancements in large language models (LLMs), agentic automation, and intelligent orchestration, AI can fundamentally reshape how agencies approach risk assessments, authorization workflows, and lifecycle governance. At REI Systems, we believe that AI is not a future ideal—it’s already demonstrating real mission value in modernizing software oversight across the federal landscape.

Is AI Ready for accelerating Software Governance & Risk Management? Absolutely

AI’s maturity has crossed the threshold from promising pilot to operational workhorse. LLMs have been shown to extract, map, and summarize security control evidence from large text artifacts like SSPs and SARs with over 90 percent accuracy. ML classifiers enriched with threat feeds routinely surface high risk CVEs with over 85 percent precision, reducing manual triage efforts by up to 70 percent. More recently, Agentic AI, a paradigm where specialized AI agents collaborate to execute tasks, brings structure and modularity to software risk workflows. Whether it’s scanning documents for compliance gaps, correlating software bills of materials (SBOMs) with known vulnerabilities, or drafting authorization documentation, agent-based approaches are purpose-built for the multifaceted needs of new-age software risk management.

For example, REI Systems’ work with DoD already leverages Azure OpenAI APIs to analyze over 60,000 test cases across disparate systems, streamlining validation with a 70% productivity gain. Combined with graph neural networks, NLP-powered search agents, and risk-aware decision engines, AI now reliably performs high-trust functions like continuous monitoring, anomaly detection, control verification, and automated Plan of Action and Milestones (POA&M) generation.

These are not hypothetical capabilities—they are live, federal-tested solutions. And the implications for software governance and risk management are profound.

Innovation Opportunities Within SWFT

REI Systems has identified ten high-impact AI applications within the Risk Management Framework (RMF) lifecycle that directly align with our federal customers’ software governance practices across a diverse range of agencies. These represent immediate, actionable opportunities to embed intelligence into the government’s software assurance processes:

1. AI-Powered Control Verification: LLMs ingest SSPs, STIG checklists, and SBOMs, then tag control evidence automatically. Gaps are flagged via dashboards, enabling assessors to focus on anomalies.
2. Intelligent Vulnerability Prioritization: AI correlates vulnerability scan data with mission impact to triage risks. Dashboards visualize CVEs by criticality, enabling smarter, faster risk decisions.
3. SBOM Normalization and Supply Chain Analysis: Using AI to normalize SBOM formats and detect risky components via graph analytics ensures supply chain transparency and resilience.
4. Mission Impact Verification: AI agents cross-validate impact classifications (per FIPS 199) against system metadata, producing required artifacts with minimal manual effort.
5. Real-Time Continuous Monitoring: AI-driven telemetry ingestion and time-series anomaly detection keep risk awareness current, driving continuous ATO viability.
6. AI-Augmented POA&M Management: Automation tracks remediation progress, forecasts delays, and recommends mitigation strategies, enhancing accountability and closure rates.
7. Automated Document Generation: Retrieval-augmented generation (RAG) drafts SSPs and SARs from historical artifacts, reducing documentation time by over 70%.
8. Network Configuration Compliance: Live configuration pull via APIs, AI validation against STIGs, and auto-remediation suggestions ensure up-to-date interconnection security.
9. Security Test Orchestration: AI coordinates scans and reviews across pipelines, mapping results to control requirements and populating risk management dashboards.
10. AI-Enabled Risk Scoring: ML models analyze historical RMF data to forecast risk, generate decision briefings, and streamline final authorization steps.

Collectively, these use cases transform federal software governance and risk management into a scalable reality—reducing labor, accelerating ATOs, and improving the consistency and quality of risk assessments.

Risks and Challenges that Demand Attention

As with any transformative technology, the implementation of AI in accelerated software risk management is not without hurdles. REI Systems identifies key risks and mitigation strategies necessary for success:

• Adversarial Attacks: AI models can be manipulated through data poisoning or prompt injection. Ensuring model robustness and conducting adversarial testing is essential.
• Data Privacy and Governance: AI will touch sensitive datasets. Implementing federated learning and rigorous governance frameworks ensures compliance and security.
• Explainability and Trust: Authorization Officials must justify AI-driven decisions. Solutions must include transparent logic trails, rationale audits, and human-in-the-loop workflows.
• Toolchain and Dependency Contamination: Vulnerabilities in AI toolsets or SBOM generators can compromise assessments. End-to-end vetting of third-party tools is vital.
• Standardization Challenges: Competing SBOM formats (SPDX vs. CycloneDX) and inconsistent outputs hinder automation. Driving consensus on standards is key.
• Cultural Resistance: Change management is required. AOs, assessors, and engineers must be trained to view AI as a partner, not a threat to authority or accuracy.
• Integration Complexity: Legacy systems currently enabling software risk management are not designed for dynamic AI inputs. APIs and translation layers must be carefully engineered.
• Talent Gaps: Recruiting AI-savvy engineers with security clearances remains difficult. A robust public-private partnership model can help bridge this gap.

Addressing these challenges requires a comprehensive operational, technical, and human strategy. But with careful planning, none are insurmountable.

Operationalizing AI-accelerated Software Risk Management

Here is what federal agencies should do to operationalize AI-drive software governance:

1. Start with High Impact Areas
   Target workflows like document parsing or vulnerability triage before scaling AI across larger pipelines.
2. Build Modular Ecosystems
   Create plug and play components including document intelligence, SBOM scanners, semantic query tools, and continuous evidence aggregators.
3. Apply cATO Thinking Early
   Capture continuous evidence streams from the beginning. A small tracker dashboard counts more than a massive static ATO binder.
4. Use Federated and Explainable Models
   Train AI models across agencies, preserve data privacy, and ensure outputs are understandable to both humans and oversight systems.
5. Govern Responsibly
   Wrap AI development in governance including data validation, bias reviews, auditability, and human oversight.

REI as an AI-enabled Software Risk Management Leader

REI Systems is proud to be a leader in accelerating secure software delivery for the federal government. With a track record of delivering production-grade AI solutions across DoD, HHS, FDA, GSA, and more, REI combines innovation with mission intimacy.

Our AI-powered test automation, NLP-infused risk triage, and agent-based document generation tools are already helping agencies slash costs, compress timelines, and improve software assurance quality. Through our Responsible AI framework and Mindful Modernization™ approach, we bring both ethical rigor and engineering discipline to every engagement.

As the federal agencies chart their own journeys toward full AI-accelerated software governance and risk management, REI stands ready as a trusted partner—empowering the mission with AI that is transparent, secure, and impact-driven.