Heuristics vs AI Detection: What Actually Changed for Red Teams
Learn how AI-based security differs from traditional signature-based and heuristics-based detection, changing how alerts, risk, and evasion work in modern environments.
Security teams have used behavior-based detection for a long time. Heuristics, rules, and correlation engines are not new. So when vendors talk about AI-enabled security controls, it is fair to ask what actually changed. From a red team perspective, the answer is not about new signals. It is about how decisions are made.
AI-based controls change how risk is calculated, how context is applied, and how evasion works in practice. Understanding that difference is critical if a red team wants to stay effective.
Follow my journey of 100 Days of Red Team on WhatsApp, Telegram or Discord.
Why this is not just heuristics detection?
Heuristic detection looks at behavior and combinations of signals. That part is not new. The key difference with AI-enabled controls is how those signals are weighted, correlated, and adapted over time.
Heuristics use fixed logic
Heuristic systems rely on rules defined ahead of time. Each rule has a known purpose and usually a known threshold.
For example:
If PowerShell runs with certain flags, add risk.
If a process spawns another process in an unusual way, add risk.
If several rules trigger together, raise an alert.
Even if the logic is complex, it is still static. The same behavior produces the same result every time unless a human changes the rules.
AI-enabled systems learn what “normal” looks like
AI-based systems build a baseline from historical data. They learn what is common in that specific environment.
For example:
PowerShell may be normal for IT admins.
The same PowerShell usage may be rare for finance users.
Some process chains may be common on servers but not on laptops.
The model adjusts risk based on how activity compares to that baseline, not just whether a rule matches.
Risk is continuous, not step-based
Heuristic systems tend to work in steps. A rule triggers or it does not.
AI-enabled systems assign gradual risk.
One action adds a little risk.
Another adds a bit more.
The alert fires only when the combined score crosses a threshold.
From a red team perspective, this matters because:
You can influence risk without fully avoiding detection.
Small changes can shift outcomes.
Behavior matters more than individual actions.
Heuristics explain decisions, AI often does not
With heuristics, defenders can usually tell you why something fired. A rule name, a condition, or a signature is visible.
With AI-based detection:
The reasoning is often opaque.
Vendors may not expose which signals mattered most.
The same action may be benign one day and suspicious another, depending on context.
This uncertainty is exactly what red teams exploit.
These differences change how evasion works and this is why adversarial attacks against AI-enabled security controls deserve separate attention. They are not about attacking the model itself. They are about understanding how risk is calculated and learning how to influence it. Red teams that treat AI-based detection like traditional heuristics will miss opportunities.
TL;DR
- Traditional heuristic detection uses static rules and known indicators whereas AI-enabled security learns normal behavior within the environment.
- With AI-enabled detection the context matters. Same action can have different risk levels for different users or times.
- Risk is treated as a continuous value, accumulating gradually rather than in discrete steps.
- Explainability is limited in AI systems; outcomes depend on context, not just explicit rules.Follow my journey of 100 Days of Red Team on WhatsApp, Telegram or Discord.