Hot take: Most EDR's 'AI' is just fancy pattern matching

Having spent the last quarter instrumenting and load-testing a major EDR's telemetry pipeline for a client migration, I've reached a conclusion that's both obvious and controversial upon articulation: the core "AI/ML" detection logic in most mainstream EDRs is, architecturally speaking, an over-glorified pattern matching engine with a statistical wrapper. The latency profiles and dataflow I observed are more consistent with high-speed rule evaluation than any model inference that would justify the marketing buzzwords.

Let me clarify. I'm not dismissing the engineering achievement. Performing real-time correlation across process ancestry, file writes, registry modifications, and network connections at the scale of hundreds of thousands of endpoints is a monumental feat. My contention is with the framing. When we benchmark "AI detection" in these systems, we're often measuring the performance of a sophisticated rules engine, likely backed by a Rete algorithm or a derivative, evaluating thousands of compiled condition-action pairs against a normalized telemetry stream. The "machine learning" component is frequently relegated to:
* **Offline model generation:** Clustering of historical data to produce new detection signatures, which are then deployed as... you guessed it, patterns.
* **Statistical outliers:** Flagging events that fall outside learned baselines (e.g., "this user never runs PowerShell at 3 AM"), which is essentially pattern matching against a dynamic, learned threshold.

Consider the data pipeline. Raw sensor data is heavily filtered and normalized client-side before being shipped. The server-side "detection" often resembles a complex event processing (CEP) system. You can infer this from the latency breakdowns. A true, heavyweight neural model inference on a single event with its full context would introduce multi-second latency, which is untenable. What we see instead is sub-100ms evaluation, pointing to deterministic logic.

```python
# Simplified conceptual model of what's often happening, not actual vendor code
def evaluate_telemetry(event: TelemetryEvent, rule_engine: RuleSet) -> Alert:
# Feature extraction (not ML inference)
features = {
"process_hash": event.process.sha256,
"parent_process": event.parent.name,
"action": event.action,
"target_path": event.target.path,
"anomaly_score": lookups.get_historical_anomaly_score(event.user)
}

# This is the core "AI" - a high-speed rule match
# The 'rules' here may be compiled from 'models' trained offline
matched_rules = rule_engine.evaluate(features)

# Post-match 'confidence' scoring might have a statistical layer
for rule in matched_rules:
rule.confidence = apply_ensemble_calibrator(rule, features)

return generate_alert(matched_rules)
```

The practical implication is that detection engineering becomes an exercise in understanding and potentially manipulating this pattern-matching foundation. The "behavioral AI" tag often means the vendor has a team manually crafting and curating these behavioral rules (or "models") based on their internal research. This isn't to say it's ineffective—a well-tuned pattern matcher with a vast, updated ruleset is incredibly powerful. But it demystifies the black box. It also explains why "bypasses" often involve breaking the expected pattern chain or introducing seemingly benign noise to fracture the event correlation.

My question to the detection engineers and architects here: does this alignment with observable performance characteristics match your experience? And if so, should we be pushing for more transparency in vendor datasheets, distinguishing between true real-time model inference and advanced, ML-assisted pattern matching? The architectural and scaling implications for deployment are significant.

--perf