Platform
Proofpoint Nexus

AI THREAT DETECTION PLATFORM

Stop Novel Attacks with Behavioural AI Threat Detection

Understand attacker intent with multi-model AI that detects what other tools miss.

Request a demo See Nexus AI in action
nexus

INTENT-BASED DETECTION

Detect more threats with intent-based detection built for deeper context

Effective AI threat detection requires more than analysing messages and behavioural signals in isolation. Multiple behavioural AI models can analyse language, relationships, visual content, patterns, and threat intelligence in parallel, then unify those signals to better understand attacker intent. This multi-model approach helps detect novel and complex threats while providing clearer, more explainable verdicts.

Detect more novel threats

Combine behavioural AI, threat intelligence, and multiple signal types to identify attacks that isolated detection can miss.

detect more novel threats

Understand attacker intent

Analyse behaviour, relationships, and content in context to uncover risk that appears legitimate when viewed alone.

Understand attacker intent

Get verdicts you can explain

See the contributing signals, risk score, and classification behind every verdict, not just the final score.

Get verdicts you can explain
01 04

THE GROWING CHALLENGE OF SCALE

Attackers are scaling novel threats faster than static detection can adapt

Attackers are abusing AI to rapidly launch personalised phishing, business email compromise, and impersonation attacks. These attacks look like normal activity, so they evade static rules and signatures when there is no known pattern to match. In addition, when detection tools analyse messages, behaviour, and other signals in isolation, they can miss the context needed to distinguish suspicious patterns from legitimate activity.

Stopping these attacks requires understanding intent and behaviour with AI built to continuously learn as threats evolve.

46 %
year-on-year increase in the volume of email threats

Proofpoint, 2026

41 %
of organisations say they can't correlate threats across channels

Proofpoint, 2026

4.5 x
higher click rate for AI-generated phishing vs. traditional phishing

Microsoft, 2025

PLATFORM CAPABILITIES

Turn multiple AI signals into one confident threat verdict

Proofpoint Nexus AI brings together five specialised behavioural AI models in one evaluation engine to detect threats across email and collaboration channels. The models analyse different signals in parallel, then combine their findings with platform-wide context to identify patterns and better understand attacker intent. The evaluation engine weighs these signals across 1,000+ attributes to deliver a single verdict and threat classification that analysts can understand and act on.

Every 60 seconds, Proofpoint detects a threat other email security providers miss, including:

27 %
more never-before-seen threats
91 %
of advanced threats other providers miss
97 %
of BEC threats other providers miss

Features

Nexus Language Model (LM)

Analyse language, urgency, context, and intent to identify patterns associated with phishing, BEC, and social engineering.

Nexus Relationship Graph (RG)

Analyse user communication patterns and relationships to detect behavioural anomalies, volumetric changes, and risky sharing.

Nexus Computer Vision (CV)

Analyse visual content to detect phishing sites, malicious QR codes, spoofing, and threats that text-based detection can miss.

Nexus Machine Learning (ML)

Recognise known attack patterns and uncover new anomalies with multiple ML techniques and predictive threat detection.

Nexus Threat Intelligence (TI)

Apply real-time threat intelligence to identify emerging threats and tactics, and sandbox suspicious URLs and attachments.

Nexus Evaluation Engine

Combine all five behavioural AI models into one explainable verdict, and continuously improve detection with every email analysed.

01 04
Nexus AI models attacker detection

ADAPTIVE AI

Continuously learn from new threats to improve detection

Nexus AI goes beyond one-time model scoring by feeding new verdicts, campaign attribution, and human threat research back into its behavioural AI models. This ongoing feedback loop helps improve pattern recognition and adapt detection as attacker tactics evolve, with no need for manual tuning.

Nexus AI combines continuous learning with proven performance at scale:

  • 99.999% detection efficacy
  • 1-in-19.7 million false-positive rate
  • 2.3 trillion emails scanned

Features

Predictive Threat Detection

Eliminate blind spots and catch more real threats with ML-driven imposter classification and predictive URL and malware sandboxing.

Behaviour-Aware Detection

Surface novel attack patterns with relationship graphing, malicious URL context, web isolation, and advanced credential phishing detection.

Multi-Channel, Multi-Stage Defence

Detect multi-stage attacks across email, cloud, web, and collaboration, including email bombing, prompt injection, and cross-channel campaigns.

01 04

FULL LIFECYCLE COVERAGE

Extend detection intelligence across every layer of protection

Apply the same behavioural AI models and evaluation engine before and after delivery, and when users interact with content. Continuous threat evaluation helps stop attacks that evade earlier controls or become malicious after delivery, and enables industry-first pre-delivery detection for AI prompt-injection attacks.

Pre-Delivery Protection

Detect advanced threats like AI prompt injection and subscription bombing before they reach the inbox.

Post-Delivery Protection

Detect and remediate threats that become weaponised after delivery, such as redirect attacks and payload swaps.

Click-Time Protection

Stop malware and credential theft at click time with URL rewriting, browser isolation, and sandboxing.

Login Time Protection

Prevent account takeovers by blocking attempts to submit corporate credentials to unsanctioned sites and web apps.

01 04

WHY PROOFPOINT

Proofpoint behavioural AI vs. traditional threat detection

Capability Traditional Threat Detection Proofpoint behavioural AI
Novel threat detection Matches activity against known rules, signatures, and threat patterns Analyses multiple signals and behavioural patterns to detect never-before-seen threats
Visual threat detection Text and signature analysis has limited visibility when malicious signals are hidden in visual content Detects phishing and impersonation hidden in images, QR codes, and spoofed pages beyond text
Attacker intent Evaluates threats with less behavioural and contextual information Combines behavioural signals with platform-wide context to better understand attacker intent
Threat context Analyses individual signals without the same unified, multi-model context Correlates language, relationships, visual content, patterns, and real-time threat intelligence
Explainable verdicts Provides detection results without the same multi-model explanation of contributing signals Shows the contributing signals, risk score, and classification behind each verdict
Adaptive detection Rules and signatures require new threat patterns to be identified and incorporated Continuously learns from new verdicts, campaign attribution, and human threat research

Connected protection for account takeover, supplier, messaging, and impersonation risks

Protect people and data with behavioural, multi-model detection across the Proofpoint platform, not just the inbox.

Learn more about our platform

REQUEST A DEMO

Detect novel threats, understand intent, and get actionable verdicts with multi-model behavioural AI. Request a demo to see it in action.

RESOURCES

Get the latest from Proofpoint

Not all AI is created equal: why context and layers matter in email security 

Read the blog

Check out all our industry insights, reports, and press releases 

Learn more
01 02

Frequently Asked Questions

AI improves threat detection by analysing large volumes of data and identifying patterns that rules and signatures may not recognise. AI-driven threat detection can connect behaviour, relationships, content, and other signals to detect anomalies and cybersecurity threats.

Proofpoint adds behavioural AI, real-time threat intelligence, and platform-wide context to this analysis. This helps identify attacker intent, reduce false positives, and produce explainable threat verdicts that security teams can act on.

AI can help detect phishing attacks by analysing message content, sender behaviour, relationships, URLs, attachments, and visual signals for signs of malicious intent. Behavioural analysis and machine learning can identify suspicious activity that rules and signatures may miss.

Proofpoint combines AI phishing detection with real-time threat intelligence across language, relationships, visual content, and machine learning. This broader context helps detect phishing, BEC, impersonation, malicious QR codes, and other evasive attacks.

AI-powered phishing attacks are harder to detect because attackers can quickly create personalised messages that mimic legitimate communication and avoid known threat patterns. These attacks may lack the malicious signatures, language cues, or other indicators that static detection relies on.

Behavioural AI can add context by analysing language, relationships, behavioural patterns, visual content, and threat intelligence together. This helps identify malicious intent even when individual signals appear legitimate.

The accuracy of AI-powered threat detection depends on the quality of its data, models, context, and ability to distinguish malicious behaviour from legitimate activity. Using multiple signals can improve detection while reducing false positives that add work for security teams.

Proofpoint combines behavioural AI with continuous learning and human threat research. This approach delivers 99.999% detection efficacy with a 1-in-19.7 million false-positive rate, providing precise, explainable verdicts that support faster incident response.

A threat intelligence platform collects and analyses threat data to help security teams identify cybersecurity threats, understand threat actors, and improve incident response. It turns raw data, such as indicators of compromise (IOCs), malicious URLs, and attacker activity, into actionable intelligence.

Modern platforms can also analyse data across sources to identify patterns and emerging threats. Proofpoint combines real-time threat intelligence with behavioural AI and platform-wide context to better understand attacker intent and detect novel threats that known signatures may miss.

Threat intelligence platforms help detect zero-day threats by analysing new threat data for suspicious patterns, behaviours, and indicators before established signatures are available. They can connect indicators of compromise (IOCs), threat actor activity, malicious infrastructure, and emerging campaigns to reveal new security threats.

Proofpoint combines real-time threat intelligence with behavioural AI and contextual analysis to identify novel attack patterns. This helps detect unknown threats based on behaviour and intent, rather than relying only on previously identified attacks.