AI that understands intent, not just signatures. Detect what others miss.
40% of phishing attacks use techniques with no prior signatures. IP Four deploys AI models trained on billions of emails to detect novel attacks, anomalous communication patterns, and zero-day threats through behavioural analysis rather than signature matching.
AI that learns your organisation and detects what does not belong.
From NLP content analysis to continuous model learning, our AI capabilities go beyond signatures to understand the intent behind every email.
Natural Language Processing Analysis
NLP models analyse the semantic content of every email, not just headers and metadata. Urgency language, unusual requests, emotional manipulation, and out-of-character phrasing are all detected and scored.
Communication Pattern Modelling
Normal communication patterns are modelled for every user and sender relationship in your organisation. Deviations from established patterns trigger additional scrutiny, catching attacks that look legitimate on the surface.
Real-Time Threat Scoring
Every inbound email receives a real-time threat score based on hundreds of behavioural signals. Scores are used to route emails to quarantine, flag for review, or deliver with a warning banner based on your policy.
Continuous Model Learning
AI models learn continuously from new threats, analyst feedback, and global threat intelligence. The more emails processed, the more accurate detection becomes. Models improve automatically without manual intervention.
Novel Attack Variant Detection
Attackers constantly modify their techniques to evade known signatures. Behavioural AI detects the underlying intent of an attack regardless of how the payload or message is modified between campaigns.
Explainable AI Decision Reporting
Every detection decision is accompanied by a plain-English explanation of why the email was flagged. Security teams can review, override, and provide feedback that improves future model accuracy.
From baseline modelling to continuous AI protection.
Baseline Data Collection
AI models are trained on your organisation's historical email data to establish normal communication patterns. Sender relationships, typical content types, and communication frequencies are all modelled.
Model Calibration and Tuning
Detection thresholds are calibrated to your sector, risk profile, and operational requirements. Models are tuned to minimise false positives while maintaining high sensitivity to genuine threats.
Parallel Deployment and Validation
AI analysis runs in parallel with existing filters during an initial validation period. Detections are reviewed by your team and ours to confirm accuracy before enforcement mode is activated.
Enforcement and Alert Configuration
Enforcement policies are configured based on threat score thresholds. High-confidence detections are quarantined automatically. Lower-confidence flags generate alerts for human review.
Analyst Feedback Integration
Your security team's review decisions are fed back into the model as training data. False positives and missed detections both improve model accuracy over time through supervised learning.
Quarterly Model Review
Model performance is reviewed quarterly against detection rates, false positive rates, and emerging threat patterns. Models are updated to address new attack techniques and changing communication patterns.
How AI detection has protected UK organisations from novel attacks.
Novel Phishing Variant Bypassing All Signatures
A Liverpool financial services firm was targeted by a new phishing campaign using a technique with no existing signatures. Every traditional filter passed the emails as clean.
Behavioural AI detected the unusual combination of urgency language, new sender relationship, and atypical request pattern. Emails quarantined. Campaign blocked across all variants. No credentials compromised.
Slow-Burn Account Takeover Attempt
A Cambridge technology company had an attacker send low-volume, innocuous emails over three weeks to establish a trusted sender relationship before launching a credential harvesting campaign.
Communication pattern modelling identified the unusual relationship-building behaviour as anomalous. Sender flagged before the attack phase. Campaign blocked. Attacker infrastructure reported.
AI-Generated Phishing Content Detection
A Bristol professional services firm was targeted by a campaign using AI-generated, highly personalised phishing content that passed all content-based filters due to its natural language quality.
NLP analysis identified the semantic manipulation patterns characteristic of AI-generated phishing despite the natural language quality. Emails quarantined. Staff briefed on AI-generated phishing risks.
Find out what novel phishing attacks are bypassing your current tools.
Our free AI security assessment runs your recent email traffic through our behavioural analysis models and identifies threats your current tools have missed. No cost, no obligation, results within 48 hours.