Closing the AI Trust Gap: Building a Scalable, Safe Agentic Era

AI has moved past the hype phase. Most enterprises are no longer asking if they will adopt AI. They are asking how to use it safely, where it can deliver measurable value, and what needs to be in place before they scale it.

That was the core theme of a recent Splunk webinar featuring Seema Haji, VP of Product Marketing, Peter Sprenger, Field CTO, and Hao Yang, VP of Engineering. The discussion focused on a challenge many organizations now face: the gap between AI ambition and AI trust.

That gap is showing up in real ways. During this discussion, they centered around two seemingly conflicting, but important data points. IDC projects that 1.3 billion AI agents will be active by 2028. At the same time, Splunk-sponsored research with Oxford Economics found that despite most organizations adopting AI pilots, only 34% have what they define as “trusted AI”.

Here’s what this means: AI adoption is moving quickly, but trust, governance, and data readiness are not keeping pace. If organizations want AI to improve security and resilience outcomes, they need the right building blocks first.

Hao Yang explains that successful organizations are moving beyond the hype to drive real business outcomes while focusing on disciplined execution, anchored by three essential pillars:

• A Solid Data Foundation: Ensuring the integrity of the data that fuels our models.

• Embedded Governance: Building guardrails that allow for innovation without compromising security.

• System Visibility: Maintaining full transparency into how AI systems make decisions.

Peter Sprenger shared that AI should be deployed as a force multiplier for the business, but only with clear mission alignment, measurable ROI, and strong guardrails. Trust is non-negotiable, and organizations must not let AI bypass the same accountability, architecture, and risk processes applied to other critical technologies.

We are seeing real-world impacts where it matters most:

• Accelerated Troubleshooting: Cutting observability response times by 50%.

• Enhanced Security: Reducing the time to investigate threats by half and stopping intrusions before they escalate.

• Operational Resilience: Lowering the cost and duration of service outages.

When we prioritize these foundations, we move from theoretical benefits to concrete operational value. These top questions from the "The Trust Gap: Why a Data Foundation is Fundamental to Agentic Enterprises" LinkedIn session are answered in this replay.

AI can be a powerful force multiplier, but only when it is grounded in trusted data, embedded in real workflows and aligned to measurable outcomes.