Security Operations Centers (SOCs) increasingly rely on Security Orchestration, Automation, and Response (SOAR) platforms to manage high-volume alerts, enrich telemetry, execute playbooks, and shorten incident-response cycles. However, many deployed SOAR systems remain rule dominated: actions are triggered by static if-then playbooks, threshold scores, and analyst-defined routing logic. Such deterministic automation is auditable and operationally useful for known, repetitive events, but it becomes brittle when adversary behavior shifts, telemetry quality varies, alert streams are noisy, assets have unequal business criticality, and compliance constraints differ across response contexts. This paper proposes an Intelligent Security Operations Automation Algorithm (ISOAA) for AI-enabled SOAR. The algorithm integrates probabilistic alert risk scoring, event-graph representation, constrained response optimization, governance-risk-compliance (GRC) gating, human-in-the-loop validation, and feedback-based policy improvement. The mathematical core models each alert as a feature-bearing security object, transforms heterogeneous telemetry into a state representation, estimates actionable incident probability, and selects response actions by maximizing expected security utility subject to operational cost, false-positive loss, and compliance penalty. A rule-based SOAR baseline, an ML-assisted triage baseline, and a reinforcement-learning cyber-response baseline are used for comparative analysis. Controlled benchmark results indicate that ISOAA achieves lower mean time to detect, lower mean time to respond, higher containment success, improved precision and recall, reduced false-positive automation, and lower compliance-breach rate than rule-based SOAR. The paper contributes a defensible mathematical architecture for intelligent SOC automation and offers practical deployment recommendations for risk-aware, auditable, and GRC-constrained response orchestration.