Trust and transparency critical in cloud AI security

Enterprise AI adoption has reached a critical point where automation drives security, risk, and compliance, but explainability remains essential to maintain trust, according to Forbes.

According to research highlighted by Harshad Pitkar, automated threat detection using SIEM, SOAR, and XDR enhances efficiency in cloud security, yet "automated security reduces risk only to the degree that it's predictable, consistent, and defendable by leadership."

The challenge lies in integrating explainable AI with automated security: without transparency, fast automated decisions create a "confidence gap" for regulators, auditors, and boards. Srinivas Reddy Kosna's 2025 study on explainable AI shows that trust depends more on the ability to scrutinize decision logic than on accuracy.

The article argues that separating automated security and AI explainability increases blind spots, whereas integrating them allows organizations to act quickly while maintaining accountability. Boards and GRC leaders must embed explainability into automation to align intelligence with governance, ensuring defensible, scalable, and trustworthy digital operations in an AI-driven enterprise environment.