Governing AI-Assisted Security Operations: A Design Science Framework for Operational Decision Support

Researchers propose a design science framework for governing AI-assisted security operations in high-risk environments like Security Operations Centers (SOCs), emphasizing controlled deployment before scaling. The study uses Microsoft Azure and Kusto Query Language as a technical case study, developing governance mechanisms that separate AI planning from execution while maintaining accountability, privacy, and auditability.

This research addresses a critical gap in enterprise AI governance: how organizations can safely integrate generative AI into high-stakes operational environments without introducing uncontrolled risks. The timing reflects broader industry concerns about deploying powerful AI systems in security-critical functions where failures carry substantial consequences.

The study's focus on SOCs is particularly relevant because these centers handle privileged data, make time-sensitive decisions with legal implications, and operate at the intersection of multiple technical and compliance domains. By examining KQL queries through an AI governance lens, the authors reveal that even read-only operations can introduce privacy exposure, cost overruns, performance degradation, and decision quality problems when augmented with AI agents lacking proper constraints.

The proposed solution—a governed query-broker architecture with schema grounding, templated responses, policy validation, and engineering review gates—represents a maturity model for responsible AI deployment. This framework prioritizes governance as a prerequisite to automation rather than an afterthought, establishing clear role accountability and evidence boundaries.

For enterprise technology leaders and security practitioners, this work provides concrete design propositions applicable beyond KQL to other high-risk AI implementations. The research suggests that successful AI integration in critical infrastructure requires intentional governance structures before scaling, not during troubleshooting. Organizations building AI-assisted SOCs or similar systems should expect regulatory and operational pressure to demonstrate equivalent governance rigor.

• →AI-assisted security operations require governance frameworks before scaling to production environments.
• →Read-only database queries assisted by AI still pose privacy, cost, and decision-quality risks requiring mitigation.
• →Schema-grounded retrieval, approved templates, and auditable traces separate AI planning from execution safely.
• →Engineering review boards and quality gates establish accountability and prevent unauthorized AI-driven decisions.
• →Governance maturity models for AI deployment apply across multiple high-risk operational contexts beyond security.