According to Dark Reading, by 2025, 60% of organizations will be using artificial intelligence within their IT security infrastructure. The technology’s core strength is its ability to process massive data volumes, correlate signals in seconds, and uncover hidden patterns impossible for humans to find manually. This makes AI exceptionally powerful for the initial phases of security—detecting anomalies and prioritizing alerts. However, the analysis stresses that speed does not equal certainty, and decisions affecting access, privileges, or legal evidence must remain predictable and auditable. The report, framed as a playbook for AI adoption, argues that AI should be confined to the “sense and think” plane of operations, while the critical “decide and act” plane must stay deterministic.
The AI Dilemma: Speed vs. Certainty
Here’s the thing: AI is brilliant at finding the needle in the haystack. It can scan billions of log events and spot the subtle, weird behavior that signals a breach. That’s a game-changer for overwhelmed SOC analysts. But the moment you let that same AI start blocking connections or revoking credentials automatically, you’re playing with fire. Why? Because most advanced AI isn’t deterministic. Give it the same input twice, and you might not get the same output. In a security context, that means a user could be granted access on Tuesday and blocked on Wednesday for no discernible, reproducible reason. That’s a compliance and operational nightmare. The playbook smartly maps this to the NIST CSF 2.0 framework, putting AI firmly in the “Identify” and “Detect” functions, but keeping it out of “Protect,” “Respond,” and “Recover.”
Why AI Can’t Be The Final Boss
The report lays out a brutally practical list of reasons AI shouldn’t be the ultimate gatekeeper. It’s not just philosophical; it’s about measurable risk. First, there’s model drift—providers constantly retrain models, so the AI you validated last month might behave differently today. NIST guidance explicitly warns about this. Then there’s the expanded attack surface. If AI executes policy, it becomes a target for prompt injection or data poisoning attacks. Think about it: an attacker tricking your AI security guard into opening the gates. Scary stuff. Finally, and maybe most importantly, are the audit and compliance gaps. How do you explain an AI’s “black box” decision in a courtroom or to an auditor? You can’t. And as NIST also notes, automation bias means humans will over-trust a confident AI, amplifying any errors.
The Deterministic Guardrails You Need
So, what’s the practical path? The playbook recommends embedding AI within ironclad, deterministic frameworks. Basically, let the AI think, but make rules-based systems act. A key concept is “policy-as-code”—having your security policies defined in machine-readable code, not vague documents. This becomes the single source of truth. Even if an AI recommends an action, it must pass through a Policy Decision Point (PDP) that checks it against that code. Other crucial guardrails include preserving full evidence trails for every AI-influenced decision (model version, inputs, etc.), running new models in staged “canary” environments to catch drift, and treating AI endpoints with the same security rigor as external APIs. It’s about containment, making AI a powerful but controlled tool. For industries where deterministic control is paramount, like manufacturing or industrial settings, this principle is foundational. The reliability required for, say, an industrial panel PC monitoring a production line mirrors the need for deterministic security enforcement—both demand predictable, auditable outcomes every single time.
Balance Is Everything
The ultimate message is one of balance. Organizations should first solidify their core security foundations—strong IAM, network segmentation, good telemetry. AI doesn’t replace that; it makes it more efficient. AI can draft response playbooks, summarize incidents, or prioritize patches. But it should never bypass those deterministic validation layers. The metrics should change, too. Don’t just measure how fast AI finds things; measure analyst acceptance rates of its recommendations and, critically, the reproducibility of AI-influenced decisions. Regular testing, like purple team exercises, should probe the AI’s resilience to manipulation. Look, AI offers a real advantage. It cuts through the noise. But that advantage doesn’t remove the need for reproducible policy, auditable enforcement, and human accountability. The playbook’s conclusion is spot on: gain the speed, but never surrender control. For deeper dives on governance, the broader NIST AI Risk Management Framework is the next logical read.
