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Core Platform — Real-Time Detection

🛡️ AI Threat Detection

Detect zero-days, APTs, and novel attacks before damage occurs

Traditional signature-based security misses 70% of modern attacks. HiveNet's AI engine is trained on billions of threat signals from global honeypots, dark web intelligence, and live incident response engagements — detecting novel attack patterns no signature could anticipate, in real time, with a false positive rate under 0.1%.

0.1%
False Positive Rate
< 90s
Mean Time to Detect
99.7%
Detection Accuracy
3B+
Threat Signals Trained On

What's Included

Every capability is powered by our proprietary AI engine and delivered by our team of senior security engineers.

🤖

Behavioral AI Engine

Our models learn what "normal" looks like for your environment and flag deviations in real time. Unlike signature-based tools, behavioral AI catches zero-day exploits, living-off-the-land attacks, and novel malware variants the moment they deviate from baseline.

🌐

Global Threat Intelligence

Continuous feeds from 200+ threat intelligence sources — government CERTs, ISACs, dark web monitoring, honeypot networks, and our own incident response telemetry. IOCs are operationalized within minutes of discovery.

🔗

XDR Correlation Engine

Correlates signals across endpoint, network, cloud, identity, and email simultaneously. Attacks that bypass individual tools are caught at the correlation layer — a phished credential used from an anomalous location triggering lateral movement gets caught in seconds.

Real-Time Response Automation

When high-confidence threats are detected, automated playbooks execute immediately — isolating endpoints, revoking tokens, blocking IPs, and notifying your team — all before an analyst even reviews the alert.

🧬

Malware Sandboxing

Suspicious files and URLs are detonated in isolated cloud sandboxes for behavioral analysis. Advanced evasion techniques — sleep timers, VM detection, anti-analysis tricks — are identified and bypassed using our hardened analysis environment.

🔮

Predictive Threat Modeling

Our AI correlates your current security posture against known attacker TTPs (MITRE ATT&CK) and models likely attack paths through your environment. Predicts and closes gaps before attackers reach them.

📊

MITRE ATT&CK Coverage Mapping

Every detection maps to MITRE ATT&CK techniques, giving your team a structured view of coverage and gaps. Real-time heatmap updated as your threat landscape evolves.

🔍

Threat Hunting

Proactive hunting campaigns by our analyst team using your telemetry. We look for attacker infrastructure, dormant implants, and pre-compromise indicators that automated detection misses. Monthly hunting report delivered to your team.

📱

Instant Alert Delivery

High-severity alerts delivered via Slack, PagerDuty, Teams, email, and SMS simultaneously — within 90 seconds of detection. Alert contains full context: asset, user, technique, recommended action. No login required to triage.

How It Works

01

Deploy & Integrate

Lightweight sensor deployment across endpoints, network taps, and cloud environments. API integrations with your existing SIEM, EDR, and identity provider. Full visibility achieved within 4 hours — no rip-and-replace.

02

Baseline & Calibrate

AI engine establishes behavioral baselines for your environment over 72 hours. Calibration eliminates known false positives specific to your stack. Detection models tuned for your industry's threat profile.

03

Detect & Respond

Continuous monitoring begins immediately. Threats detected in under 90 seconds. Automated playbooks execute for high-confidence detections. Human analysts review and escalate anything requiring judgment.

04

Hunt & Improve

Monthly threat hunting campaigns proactively search for dormant threats. Detection models updated weekly with new threat intelligence. Quarterly reviews with your team to align coverage with evolving business risk.

Works With Your Stack

Native integrations with the tools your team already uses — zero rip-and-replace required.

CrowdStrike Falcon
SentinelOne
Microsoft Defender
Carbon Black
Palo Alto Cortex XDR
Elastic SIEM
Splunk
IBM QRadar
Microsoft Sentinel
Sumo Logic
AWS GuardDuty
Azure Defender
GCP Security Command Center
Okta
Azure AD
Duo Security
Proofpoint
Mimecast
Abnormal Security
Jira
ServiceNow
PagerDuty
Slack
Teams

Common Questions

EDR protects the endpoint layer. HiveNet AI Threat Detection correlates signals across endpoint, network, cloud, identity, and email simultaneously — catching multi-stage attacks that bypass individual tools. We ingest your existing EDR's telemetry as one of many data sources, enriching it with broader context.

Under 0.1% — verified across our customer base. We achieve this through behavioral baselining specific to each customer environment, corroboration across multiple signal sources before alerting, and continuous model tuning from analyst feedback. Your analysts spend time on real threats, not noise.

Detection coverage begins within 4 hours of sensor deployment. Full behavioral baselines are established within 72 hours. Most customers identify their first previously-unknown threat within the first week of monitoring.

No — we integrate with it. HiveNet can forward enriched, correlated alerts to your existing SIEM, or your SIEM can forward raw logs to HiveNet for AI analysis. We work alongside your existing security investments, not against them.

We use a combination of TLS inspection at the network layer (with your authorization), JA3/JA3S fingerprinting for encrypted session profiling, and behavioral analysis of connection metadata — identifying C2 traffic and data exfiltration even within encrypted sessions without breaking privacy.

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AI Threat Detection?

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