When Ravi Soin mapped AI usage at Smartsheet, he found tools scattered across workflows. Some had official stamps. Others didn't. He asked vendors for logs of data accessed and actions taken. The responses were thin. The audit infrastructure wasn't there.
That moment changed his approach. The real risk wasn't the models themselves—it was their opacity. Security teams used to control by restriction. Bans just drove adoption underground, making visibility worse. Soin, Smartsheet's CISO, now asks a different question: "Can we trace it?" He treats each AI agent like a new hire—assign a role, limit authority, demand accountability. New employees don't make unsupervised high-stakes calls. Neither should autonomous systems.
The Cloud Security Alliance's 2026 survey of 1,500 security leaders backs him up. Sensitive data exposure tops worries at 61%. Regulatory compliance hits 56%. And 92% are concerned about AI agents loose across the workforce. Generative AI sits inside 77% of security stacks, but only 14% let AI take independent remediation without human review. When something goes wrong, boards and regulators want answers: Who approved this? How? When? Without real-time logs of queries, decisions, and overrides, those questions stall.
Audit used to mean periodic checks. Observability turned it into continuous monitoring. Agentic AI demands the next step: record every data source touched, flag autonomous actions versus approved ones, track the human in the loop—as events happen, not after. Audit can't be an afterthought. It must be the base layer of any AI strategy. Soin evaluates every tool against one standard: show the traceable chain from inputs to recommendations to sign-off. Fail that, and the tool stays out.
This builds more than compliance. It builds confidence. Employees act on AI suggestions when they see the logic and oversight. Productivity rises. Second-guessing drops. Governance stops being a bottleneck and starts accelerating teams. The EU AI Act, NIST's Risk Management Framework, and ISO 42001 all point the same direction: auditability is table stakes.
Yet gaps remain. A Thoropass survey found 82% of compliance leaders see AI as a material risk. 69% believe AI outpaces their controls. 91% have faced auditor pushback requiring evidence resubmission. Real-time auditing carries computational cost—some estimate 10x inference overhead. Budgets strain. Vendors race to offer audit features, but many just wrap LLMs in reporting interfaces. Skeptics call parts of the market a grift.
The payoff for early adopters is faster, safer scaling. Map every OAuth token and API key that signals hidden AI integrations. Set explicit rules on which decisions need human approval and revisit them twice a year. Favor vendors who embed traceability from the start and feed logs into central monitoring. When AI carries clear ownership, recorded actions, and override mechanisms, trust grows. Teams move quicker because they can verify and correct course. Trust, but verify. The old maxim applies with new force.
The test is straightforward. When an AI-driven process produces unexpected harm, can you trace exactly what happened? Security leaders who say yes without hesitation stand apart.
Source: Webpronews