Artificial Intelligence (AI) is on track to become the norm in cybersecurity. A recent Deloitte study has revealed that 84% of cybersecurity programs implement AI in some capacity. It has become nearly synonymous with tomorrow’s cyber defenses, to the point where any system that doesn’t implement some form of AI will be outdated and likely unable to keep pace with adversaries.
What’s less understood is how AI actually fits into the work of cybersecurity professionals. Many assume AI’s primary role is simply to crunch through massive amounts of unfiltered data and hand analysts the answers. In reality, humans play the defining role by shaping and preparing the data so AI can deliver accurate, actionable insights.
Contextualization Removes Ambiguity
LLMs are adept at parsing patterns in language, but when applied directly to raw security telemetry without structure or context, they often return ambiguous or misleading outputs. More often than not, all it produces are summaries, labels, and hallucinations. The AI does not understand what it’s being shown, and thus cannot make intelligent assessments or distinctions.
The key to preventing this confusion is context. This information originates from an organization’s systems of record, including vulnerability scanners, EDR, SIEM, IAM, and firewalls, among other security tools. The AI must be able to understand how all of these pieces connect in order to deliver cogent analysis. Providing context for your AI is how it can be transformed from just another alert engine to the brain behind your cybersecurity program.
Normalization of Data Builds AI Confidence
Once the data has been contextualized, it has to be structured. This is where many cybersecurity data pipelines fall short, as they often feed AI agents a chaotic mix of logs, asset lists, alerts, and tickets. There isn’t the slightest understanding of what connects to what, or why it all matters.
Semantic normalization solves this gap. It provides the foundation for a security mesh by organizing and interpreting security inputs within a shared schema. With normalization in place, the data can reflect critical relationships, such as:
- Which asset belongs to which business service
- Which controls are covering which vulnerabilities
- Which identities have access to sensitive data
- Which external paths lead to internal risk
Once you normalize that data, your AI can begin to reason, correlate, and act with a heightened level of confidence. This context is not just nice to have; it is the difference between hallucination and decision.
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Emulation Leads to Effective Threat Response
However, even if your AI is consuming data in a manner that it can understand and analyze intelligently, to assume it will know how to handle every threat thrown at it is naive. AI needs to continuously rehearse these threats in order to identify where weaknesses lie. It needs to learn from the data it digests, make connections, act on what it sees, and grow stronger with each simulation.
It’s true that AI is capable of making calculations and inferences at speeds that humans cannot match. But just like ourselves, AI learns by doing and improves with practice. By repeatedly simulating how an attacker would exploit a vulnerability, AI can build its capabilities, testing what works, what fails, and where it should focus the majority of its efforts.
Beyond the Program: How Security Teams Need to Change
Perhaps even more important than the physical changes security teams make to their AI tools is the mindset shift that accompanies them. For years, the backbone of cybersecurity has been detection rules: if X happens, trigger Y. But as modern threats and environment sprawl have shown us, those rules alone can’t keep up. With AI, data quality has become the new detection logic. This mirrors a broader shift from rule-based to reasoning-based security operations, where data fidelity, not just data volume, drives precision defense.
When it comes to AI, the focus should be on whether the results being delivered are accurate, actionable, and trustworthy within their environment. Rather than asking how smart the AI is, teams should be determining what their AI is seeing and how well it understands the information being fed to it. If the conclusion reached is unsatisfactory, it means that further contextualization and semantic normalization are needed to produce the desired results.
The Security Leader’s Role in the Future of Cyber
AI augments data analysis, threat detection, and breach mitigation at levels that would not be possible without it. However, this does not mean that human-led cybersecurity programs are a thing of the past. In fact, they’re more important now than ever.
While the future of cyber defense lies with AI, that future is only achievable with capable security teams at the helm. It is imperative that CISOs and SecOps heads understand that there will be two kinds of cybersecurity platforms moving forward: those that were built without a mind for how interactions with AI will shape their solution, and those that were purposefully constructed with AI at their core, ready to be fully optimized.
The focus must shift towards controlling and simplifying the decisions that AI makes. Understanding how AI interprets data and making the data it consumes comprehensible is crucial for the next generation of cyber protection.
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