By late 2025, it's nearly impossible to find a cybersecurity vendor or MSSP that doesn't reference AI or machine learning in its marketing materials. The technology has real applications in threat detection — behavioral analysis, anomaly detection, automated correlation, and predictive risk scoring are all areas where machine learning models outperform rule-based systems. But the gap between vendors using AI as a core detection engine and those using it as a marketing label is substantial.
For businesses evaluating cybersecurity providers, the challenge is separating signal from noise in the AI claims themselves.
Where AI genuinely improves threat detection
User and entity behavior analytics (UEBA)
Machine learning models that baseline normal behavior for individual users and entities (servers, applications, IoT devices) can detect deviations that rule-based systems miss entirely. A user who normally accesses three file shares during business hours suddenly downloading data from a fourth share at 2 AM triggers a behavioral anomaly — even though each individual action is technically authorized.
UEBA is particularly effective against insider threats and compromised credential attacks, both of which bypass traditional perimeter and signature-based defenses.
Automated alert correlation
A single security incident typically generates dozens of alerts across multiple systems — firewall logs, endpoint detection events, authentication failures, DNS anomalies. AI correlation engines can group related alerts into a single incident timeline, reducing thousands of daily alerts to a manageable queue of investigated cases. This doesn't replace analyst judgment; it gives analysts a coherent picture instead of a fragmented one.
Malware classification
Traditional antivirus relies on signature databases — known patterns of known malware. AI-based malware classifiers analyze behavioral patterns: what a file does when executed, what system calls it makes, how it communicates. This approach catches novel malware variants and polymorphic threats that signature-based detection misses.
Phishing detection
Natural language processing models trained on phishing campaigns can evaluate email content, sender reputation, URL structures, and linguistic patterns to catch socially engineered messages that bypass traditional email filters. The improvement is measurable: AI-enhanced phishing detection catches 15-30% more malicious messages than rule-based filtering alone, according to multiple vendor benchmarks.
The best AI threat detection doesn't generate more alerts. It generates fewer, better alerts — each one pre-correlated, pre-enriched, and ready for analyst decision-making.
Red flags: when AI claims are marketing
Not every vendor claiming AI-powered detection is delivering it. Warning signs include:
- No specificity about the model type. "We use AI" without explaining whether it's supervised classification, unsupervised anomaly detection, reinforcement learning, or NLP indicates the vendor may be wrapping a rules engine in AI branding.
- No discussion of false positive rates. Real ML-based detection systems have quantifiable false positive and false negative rates. Vendors who can't cite these numbers may not be running genuine ML models in production.
- No training data transparency. Effective ML models require large, diverse training datasets. A vendor should be able to describe (at a high level) what data their models are trained on and how they handle bias in detection.
- AI only in the marketing deck, not in the SOC. Ask to speak with a SOC analyst about their daily workflow. If they describe manual triage processes with no mention of AI-assisted prioritization, the AI may not have reached the operational layer.
The adversarial AI problem
AI-powered defense has an inherent challenge: attackers are also using AI. Generative AI tools can now produce convincing phishing emails free of the grammatical errors that traditional filters catch. AI-generated deepfake audio has been used in vishing (voice phishing) attacks impersonating executives. Adversarial machine learning techniques can probe defensive models to find blind spots.
This creates an arms race dynamic. A cybersecurity provider's AI capability isn't a static advantage — it requires continuous model retraining, adversarial testing, and integration of new threat intelligence. Providers who deployed an ML model two years ago and haven't updated it are protecting against two-year-old attack patterns.
Questions to ask your security provider
AI capability evaluation
- What specific detection use cases does your AI handle? (UEBA, malware classification, phishing, correlation?)
- What is your false positive rate for AI-generated alerts vs. rule-based alerts?
- How frequently are your detection models retrained?
- Can you show me an example of an incident that AI detected that rules-based detection would have missed?
- How do you test your models against adversarial AI techniques?
- Where does the AI inference run — on-premises, in your cloud, or in a third-party service?
The cybersecurity providers delivering genuine value with AI are the ones who can answer these questions with specifics — not the ones who need to check with marketing first.