Three AI governance questions every executive needs to answer
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AI is a major topic of conversation among leaders in C-suites and boardrooms across organizations of all sizes — and rightly so. Leadership teams have a mandate to quickly adopt AI, whether it comes from the board or their own convictions. Regardless of the reason why, the goal is to gain competitive and business leverage with AI while using the technology safely.
To use AI safely, there are three governance questions that every executive needs to answer. Before we get to them, I want to provide background as to why they are so important.
Everything about AI and agent technology is moving fast, and it’s causing generational change on the order of railroads, computers, and the internet. Where it differs from previous technological leaps forward is in the speed of innovation and scale of risk.
Almost overnight, software developers transformed from coders to supervisors of coding agents. AI also democratized development, creating “citizen coders” across non-engineering groups that build their own tools for their own specific use cases, with corporate data fueling it all.
Simultaneously, AI companies are enhancing the technology’s capabilities at an incredible pace. Each new release improves a model’s ability to make decisions and act autonomously. And unlike traditional software, which follows pre-determined steps, AI agents write their plans while they run. Now we’re also seeing long-lived agents working independently or together to solve difficult problems, enhance research, and engage customers.
We are also seeing AI risk increase. There are new breaches and examples of agents going rogue every week. Questions about the safety of new or enhanced AI models have led to releases being delayed. And among major AI players, concerns about safety spurred them to come together for discussions about regulation and governance.
Given the transformative speed and safety concerns of AI, it is critical that leaders understand the right questions to ask so they can balance risk and reward.
The balance between risk and reward
AI agents are force multipliers, but they can act in unpredictable ways that straddle the line between safety and recklessness. This is why leaders need to calibrate their plans for AI around two key points.
First, what your AI agents do on behalf of your team is your responsibility. Just like you manage employees, applications, and other assets, you need to manage AI. Second, your AI agents likely have access to credentials that give them permission to traverse multiple attack surfaces, including endpoint, cloud, and SaaS. That means their impact stretches beyond your environments to those of your customers and suppliers.
Sysdig Founder and CTO Loris Degioanni has shared his vision about why runtime is ground truth for AI agents. This blog post takes a different approach and focuses on the AI governance questions every executive should be able to answer.
The three AI governance questions you need to ask, and the answers you should expect
You may not need to understand kernels or system calls to lead on AI governance, but you do need to have evidence-backed answers when the board asks about how you ensure that your organization is using AI responsibly. These are questions to put to any of your teams running AI and the answers to expect in return:
- How do we know our AI isn’t going rogue? Observation at runtime is the surest way to understand an AI agent is doing and if that behavior goes against policy. Data collected at runtime is also the ideal foundation for response at the moment an agent acts.
- What AI are we using, and who is accountable for it? Your team should have a live inventory built from real-time data, not a survey, with every agent traceable to a person or team. Shadow AI, meaning AI used without formal security approval, is already present in most organizations. And unlike the shadow IT of the past, unsanctioned AI can reach almost anything across your IT infrastructure and the environments of customers and partners.
- What can we prove? You should expect an unadulterated forensic record of an agent's actions, including what they were allowed to do and who approved it. The information needs to come in a form the agent cannot alter. That record serves audits, investigations, and regulators equally.
These questions work because they are tied to outcomes, and the answers work because they are tied to observability at runtime.
Runtime is the foundation of effective AI security
Runtime security is built on the proven discipline of monitoring a container as it runs in production. In the simplest definition, AI agents are workloads, which means runtime monitoring should be pointed at them, no matter where they run. But what does that look like in practice?
It starts with correlating observability data from two layers: below the agent at the kernel and inside the agent. Each layer enriches the other to provide the most reliable signal when something goes wrong, and the context needed to respond with confidence.
Here’s why correlation and enrichment is essential for reducing agentic AI risk. Kernel data provides ground truth about actions such as processes the agent is running, the files it’s opening, and the connections it’s making. But data collected at the kernel layer only shows so much. What it lacks is context into why the agent is taking actions and on whose behalf. To fill that gap, you need to observe what’s going on inside the agent in real time to gather semantic detail.
Coding agents like Claude or Codex expose their activity through hooks, configuration files, and session data. This is where you see semantic data about the prompt behind an action, the MCP server an agent invokes, or the web search it’s running. The agent controls this layer, so the data isn’t proof on its own, and a recent report questions how reliable this information is, because “the connection between how they think and what they report on it only becomes more tenuous.”
On its own, semantic data from an agent still leaves blank spots in understanding the actions an agent has taken. But when combined with kernel-level syscall data, that data provides a complete picture of what’s happening, as it’s happening.
The final piece of the puzzle is the ability to execute machine-speed responses grounded in real-time context to stop or block an agent acting anomalously or maliciously. The same action can be trivial or catastrophic depending on the reach an agent has based on the credentials it holds. Correlated, enriched data enables your team — or the security agents they’re running — to make accurate assessments of the best response to take across laptops, cloud workloads, and third-party agent platforms.
Conclusion
Ultimately, the organizations that benefit most from AI will be the ones that can show what their agents do and be able to prove it when asked. That’s why runtime insights need to be in your security program’s DNA, not bolted onto an existing product to fill an AI-sized coverage gap. And while you don't own how fast the frontier of AI innovation moves, you do get to control the steps your organization takes to ensure responsible AI governance.