Security teams are drowning in data. Alerts, findings, metrics, dashboards — all technically “useful,” yet often failing to answer the most important question: what actually matters right now, and why? As artificial intelligence becomes more accessible across security programs, many organizations are experimenting with AI-driven tooling in the hope that it will bring clarity. But used inefficiently, AI can often amplify existing problems rather than solving them.
This discussion explores how AI is reshaping data-informed security decision-making — not as a silver bullet, but as a force multiplier that depends heavily on human judgment, context, and intent. We’ll examine why most AI in use today more closely resembles advanced machine learning than true autonomous intelligence, and why outcomes are still largely driven by the quality of inputs, assumptions, and program maturity.
Through an operational and security lens, the speakers will unpack how teams can move from raw signal volume to meaningful narratives that support better prioritization and risk decisions. We’ll discuss common failure modes, including over-reliance on automation, misinterpreted insights, and resistance from skeptical stakeholders. The conversation will also address how organizations can responsibly incorporate AI into existing security workflows without compromising trust, transparency, or accountability.
To ground the discussion, we’ll walk through a practical vulnerability management example, illustrating how an AI assistant can help surface recommendations that improve efficiency and decision quality — while still keeping humans firmly in control of outcomes. Ultimately, this session aims to help security leaders separate signal from noise, hype from value, and automation from understanding as they navigate an evolving AI landscape.
