Aurora Endpoint Security
AI-driven endpoint protection with 100% threat detection and 20x lower CPU usage.
Vendor Information
Aurora Endpoint Security Overview
Aurora Endpoint Security is Arctic Wolf's AI-native endpoint protection platform that delivers prevention, detection, and response capabilities to stop threats before they disrupt business operations. The solution achieved 100% threat protection against 1,000 recent malware samples in independent Tolly Group testing while using 33% CPU utilization during scanning—almost half the resources of industry benchmarks. Powered by Alpha AI, the longest-running predictive AI model in the cybersecurity market, Aurora makes pre-execution decisions in 30 milliseconds by analyzing up to six million unique features per binary.
Arctic Wolf, founded in 2012 and headquartered in Eden Prairie, Minnesota, has raised $899M in funding and achieved unicorn status with a $4.3B valuation. The company serves over 5,500 customers globally and operates one of the world's largest commercial SOCs. Aurora Endpoint Security is built on the Arctic Wolf Aurora Platform, which processes trillions of weekly security observations from 10,000+ customers to continuously improve threat detection and response capabilities.
The platform includes four solution tiers: Aurora Protect (EPP), Aurora Endpoint Defense (EDR), Aurora Managed Endpoint Defense On-Demand, and Aurora Managed Endpoint Defense with 24x7 SOC monitoring. Aurora eliminates operational friction through lightweight agents optimized for performance, with model updates occurring every several years rather than daily. The behavioral detection engine provides high-fidelity detections with minimal false positives, covering the entire MITRE ATT&CK framework and reducing alert fatigue by 90%. Organizations can deploy Aurora across Windows, macOS, Linux, iOS, Android, and Chromebooks with full protection even when endpoints are offline.
Key Capabilities
Standardized capabilities mapped to this product's security niche
Executes isolation, process kill, or persistence removal actions automatically upon detection without waiting for analyst approval. Speed of automated response directly affects breakout time mitigation.
Detects threats by modeling process behavior, memory access patterns, and inter-process relationships rather than matching file signatures. Catches novel malware and LOLBin-based attacks that have no signature.
Provides equivalent detection coverage, behavioral analysis depth, and response capabilities on Linux and macOS agents as on Windows. Most platforms have a material detection gap on non-Windows systems.
Vendor security analysts proactively hunt for attacker TTPs in the customer's telemetry on an ongoing basis, distinct from automated detection and alert response.
Captures and analyzes in-memory process state to detect fileless malware, injected shellcode, and credential material that leaves no disk artifacts. Requires kernel-level agent access.
Maintains local detection and prevention capability when the endpoint cannot reach the management console, relevant for air-gapped, traveling, or connectivity-impaired devices.
Provides a query interface over telemetry (process tree, network connections, registry events, file events), for analyst-led investigation independent of alert workflows. Differentiation is query language expressiveness and historical data retention.
Extends the agent and telemetry model to cloud VMs, containers, and serverless functions, providing consistent detection and response capabilities across on-premises and cloud workloads.
Ingests events from non-endpoint sources (firewall, identity, email, cloud) into the EDR platform for cross-signal correlation, enabling XDR-style detection without a separate XDR product.
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