Debates on AI security and rule-based tools are often framed as an arms race. AI is depicted as the future and rules as the past, suggesting a zero-sum game. I think that framing is misleading, and it forces organizations to make poor decisions about how they want their security architecture to fit together.
AI security & rule-based tools are designed to do different things. They are not improved and degraded iterations of the same capability. It is the precondition that grounds your ability to do both effectively.
What Rule-Based Security Tools Are
Rule-based security tools detect threats by comparing what they observe with a set of predefined criteria. Whether it is an intrusion detection system monitoring some specific network signatures, a firewall filtering traffic with respect to known malicious IP addresses, or any SIEM platform triggering an alert when a predefined sequence of log events occurs, all the abovementioned work on the same basic principle. The tool is predetermined on what it intends to find and a warning chimes when it does.
This model has real strengths. In the case of a good rule, the alert it fires is very specific and actionable. Security teams have full visibility into what the rule matched and what response is dictated. Rule-based detections are explainable, auditable and deterministic. No training data or model tuning is required for them.
The basic limitation is, of course, structural. A rule can only detect what it was written to discover. When an attacker uses a technique that has never been managed to be converted into a rule, the activity passes silently. By design, signature updates take a while to address this, always trailing behind new attack methods by the amount of time it takes to observe, analyze and encode a new technique.
What is AI security covers how organizations are putting AI to work protecting systems today, along with the shift in detection capabilities now available to security teams.
How AI Security Tools Actually Work
AI security tools operate on a whole different level. They do not measure observed activity against a static list of bad patterns; instead, they create a model of normal activity across the environment and can identify deviations from that model.
The model is constructed from real data: network flows, user access history, process execution sequences, file access records, and API call patterns. A machine learning algorithm will build a statistical model of normal operation. If behavior is outside the preview of what was expected, then it triggers an investigation.
This yields the following key property for the coverage of uncertainty. An AI detection system does not require having previously seen a particular attack technique to identify it. If the technique produces behavior outside what looks normal to the model, it gets a hit even if it doesn’t appear in any signature catalog. This is what structurally differentiates AI-based detection from rule-based detection: not speed or scale, but the types of threats each can even detect at all.
Where Each Approach Falls Short
Both approaches are egregiously limited in different ways, and knowing those limitations may be just as important as knowing their strengths.
When requests that trigger the conditions defined in a rule occur in benign contexts, this results in false positives for such tools. A rule created to find a certain kind of port scan could be triggered on genuine scans conducted by in-house IT teams. False positives are handled by incremental refinement of your rules, additional exception conditions and tuning thresholds. Throughout, the logic is transparent and auditable.
AI detection tools have a different kind of false positive problem. When a model has observed that an account’s activity has been normal for some time, detecting any actual anomaly in that account may warrant an alert. The baseline is real, the deviation is real, but not all of those very real deviations in isolation represent a threat. The baseline of the model may lag in rapidly changing environments, through new software deployments, personnel changes or infrastructure scaling, leading to increased false positives while the system adjusts.
The issue of explainability with regard to AI detection is also a heavy load. The analyst knows exactly which condition was matched when a rule fires: An AI system flags anomalous activity, but the justification is due to interacting features that may be difficult to explain. This is addressed by some AI detection platforms that surface which signals were most responsible for the anomaly score, but the explanation of how they impacted the score is seldom as cut-and-dry as a rule match.
AI depends on data, so AI security has specific requirements that differ from rule-based tools. To build an accurate behavioral baseline, you need data from the environment – ideally enough historical data that can reflect behavior in your operational context. They begin with very thin baselines and require time to mature into reliable detection signals or systems just recently been integrated into the environment.
The Dual Targeting of Risk Management Frameworks
The gap between rule-based and AI-based detection is a recognized governance concern at the level of national security frameworks. The NIST AI Risk Management Framework addresses the governance, transparency, and reliability considerations that apply specifically to AI-based systems, considerations that are less prominent in traditional rule-based tool governance because the detection logic is explicit. AI systems require ongoing monitoring for performance drift, training data integrity, and output reliability in ways that static rule sets do not.
The CISA AI security guidance frames AI as a tool that must itself be secured, following secure-by-design principles applied to any critical software system. The guidance reflects recognition that AI security tools introduce their own attack surfaces and governance requirements that organizations must manage explicitly.
Both frameworks do not consider AI a replacement to established security tooling. Both view it as a category of capability with distinct properties, risks, and governance responsibilities that supplement existing approaches.
The Right Architecture Uses Both
The way that this approach differs from the artisanal approach has a pretty important practical implication: The best enterprise security architectures employ both of these approaches but apply them selectively based on their raw applicability to the types of detection problems being faced.
- Rule-based tools perform the type of detection they are best suited for: identifying known attack patterns with minimal false positives to enforce a particular policy condition, creating an explainable alert that can be triaged in seconds and offering standardized coverage that audit and compliance rely on.
- AI tools do what rule-based tools cannot, including recognizing new methods without ever having been converted to signatures, surfacing unusual behavior on accounts and systems that look normal from a rules point of view and correlating among millions of telemetry points to see an indication that no single rule would catch.
Frequently Asked Questions
Will AI security tools completely end the need for SIEM rules?
No, they will not be replaced, and replacing them completely would eliminate capabilities not reproduced by AI detection. SIEM rules offer visibility into known attack patterns and compliance conditions that require consistent, auditable logic with precise, explainable detections. This is complemented by covering anomalous behavior that even the most expert humans will never anticipate through the use of AI detection as well.
What about new employees who have no baseline (do not exist in historical records)? How does an AI security model handle this?
For new emerging entities detection can only start after a period in which data is collected, thus most behavioral AI systems need time to learn. For more, they apply peer-group baselines, where the actions of similar accounts or systems act as a proxy for behavior until sufficient individual history is built. Both produce a new entity and indicate that there is a detection gap during the early period.
Is it more difficult to conduct post hoc investigations of AI-generated security alerts than of rule-based alerts?
Often, yes. Rule-based alert indicates the condition that matched. An alert generated by an anomaly in AI – it is a behavioral or situational change that would be very difficult to understand without additional context. High-quality AI detection platforms will show supporting evidence alongside the anomaly score, but familiarity with the tool is typically necessary for analysts to quickly triage AI alerts.
