Power cybersecurity AI from threat detection to autonomous SOC operations
Zilliz Cloud is a fully managed vector database that powers malware similarity detection, threat intelligence matching, and semantic alert triage for security operations â with sub-100ms latency across billions of threat indicators. Security teams use it to identify malware variants that signature-based tools miss, correlate threat intelligence across feeds by meaning, and reduce alert fatigue by clustering similar incidents automatically. Proven in production at Trend Micro (tens of millions of APK samples, <95ms query latency). SOC 2 Type II, ISO 27001, and HIPAA-ready. BYOC deployment for air-gapped and high-compliance environments.
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AI Capabilities for Next-Generation Security Operations
Every hard cybersecurity problem is fundamentally a similarity problem: find code that behaves like known malware, find network activity that resembles past intrusions, find alerts that cluster into the same incident, find threat intelligence that matches what you are seeing now. Zilliz Cloud gives security teams the infrastructure to solve these with true semantic similarity â at the speed and scale that modern threat landscapes demand.
Identify Malware Variants That Signature Matching Misses
Encode executable behavior, API call sequences, and binary features into vector embeddings and search for nearest neighbors against a database of known threats. Catch polymorphic and metamorphic malware that evades hash-based and YARA rule detection â the same approach Trend Micro uses to analyze tens of millions of Android APKs in real time with Milvus.
Correlate Indicators Across Feeds by Meaning, Not Exact Match
Embed threat intelligence reports, IOCs, and TTPs into a shared vector space and find semantically related indicators across multiple feeds. Match a novel phishing domain to a known campaign even when URLs, IPs, and hashes have all changed â because the behavioral pattern and infrastructure fingerprint remain similar in embedding space.
Cluster and Prioritize Alerts by Behavioral Similarity
Embed security alerts into vectors that capture context, not just severity tags. Automatically cluster related alerts into incidents, surface the ones that are semantically similar to confirmed true positives, and suppress the ones that cluster with known false positives. Reduce alert fatigue for SOC analysts drowning in 11,000+ daily alerts where only 19% are worth investigating.
Detect Phishing Campaigns Through Visual and Textual Similarity
Embed phishing emails, landing pages, and brand impersonation assets into vectors and search for near-duplicates of known campaigns. Catch lookalike domains and cloned login pages that bypass URL blocklists by detecting visual and textual similarity to legitimate assets â even when attackers rotate infrastructure daily.
Map Vulnerabilities to Exploits and Affected Assets Semantically
Embed CVE descriptions, exploit code, and asset configurations into a shared vector space. When a new vulnerability drops, instantly find which of your assets have semantically similar configurations, which existing exploits target similar code patterns, and which past incidents involved comparable attack surfaces â replacing manual cross-referencing across siloed tools.
Search Logs and Investigations by Meaning, Not Keywords
Embed security logs, incident reports, and forensic artifacts into vectors that capture semantic meaning. Let analysts search investigation history with natural language queries â 'lateral movement after credential theft via RDP' â instead of constructing complex regex patterns across disparate SIEM data sources that miss syntactic variations of the same behavior.
Why Zilliz?
Why security teams choose Zilliz Cloud
Cybersecurity has three infrastructure requirements that most databases cannot meet simultaneously: sub-100ms latency for real-time detection in the threat pipeline, scale to index billions of IOCs and historical threat artifacts, and continuous updates as new malware samples and threat intelligence arrive every second. Legacy approaches â signature matching, YARA rules, regex-based SIEM queries â cannot keep up with adversaries who use AI to generate polymorphic malware, rotate infrastructure hourly, and launch multi-stage attacks that look benign individually. Trend Micro runs their APK malware detection system on Milvus with <95ms query latency across tens of millions of samples and hundreds of thousands of daily additions. Vector similarity search gives security teams the same advantage: detect by behavior and meaning, not by brittle exact matches.
<95msQuery Latency
Detect threats within real-time pipeline latency budgets
Inline threat detection requires results before the network packet, email, or file reaches the user. Zilliz Cloud delivers sub-95ms query latency â proven in Trend Micro's production malware detection pipeline. Fast enough for inline scanning, real-time alert enrichment, and automated triage without adding perceptible delay to security workflows.
10B+Threat Indicators
Index billions of IOCs, samples, and historical artifacts
Enterprise security operations accumulate billions of indicators of compromise, malware samples, log entries, and threat intelligence records over time. Zilliz Cloud supports tens of billions of vectors in a single index â your entire threat history, IOC database, and malware corpus searchable together without sharding workarounds.
100K+QPS
Handle detection queries at network-speed throughput
High-throughput environments â email gateways, network sensors, endpoint agents â generate hundreds of thousands of events per second that each need similarity scoring. Zilliz Cloud handles 100K+ queries per second so that every file hash, URL, and behavioral signature can be checked against your threat database without becoming a bottleneck.
BYOCAir-Gap Ready
Deploy in your own cloud for classified and regulated environments
Security teams in government, defense, and critical infrastructure cannot send threat data to shared cloud infrastructure. Zilliz Cloud BYOC deploys the fully managed service inside your own VPC â data never leaves your environment. SOC 2 Type II, ISO 27001 certified, HIPAA-ready, with support for FedRAMP-aligned deployment patterns.
Hybrid search with metadata filtering
Combine semantic vector similarity with structured filters â match malware samples filtered by file type and detection date, search threat intelligence filtered by source feed and confidence score, or retrieve alerts filtered by severity and asset criticality. One query, both signals.
Real-time index updates
Threat landscapes change every second. Zilliz Cloud supports continuous vector ingestion without performance degradation â keeping your malware database, IOC index, and threat intelligence embeddings current as new samples and indicators arrive, not hours-stale.
Multi-vector and multimodal search
Security artifacts are multimodal: a phishing attack has an email body (text), a landing page screenshot (image), URL patterns (structured), and behavioral telemetry (time-series). Search across multiple embedding types in a single query to catch threats that span modalities.
Automatic and elastic scaling
Security events spike during active incidents, campaigns, and zero-day disclosures. Scale compute up for high-activity periods and back down automatically â handling the 10x traffic surge during an active attack without pre-provisioning idle capacity.
Multi-tenant architecture
Serve multiple business units, clients, or classification levels from a single deployment. Fine-grained isolation ensures one tenant's threat data, detection models, and investigation artifacts never leak to another â critical for MSSPs and multi-division enterprises.
Enterprise-grade compliance and reliability
SOC 2 Type II certified, ISO 27001 certified, GDPR compliant, HIPAA-ready, 99.95% SLA. BYOC deployment available for security teams with strict data residency, air-gap, and compliance requirements under FedRAMP, ITAR, and CJIS frameworks.
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Resources
Essential reading for cybersecurity AI teams
Explore how security teams use vector search for malware detection, threat intelligence, and anomaly detection â with production case studies and technical architecture guides.