GravityZone Business Security

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 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.

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.

Delivers detection, behavioral analysis, and response across Windows, macOS, and Linux agents, with non-Windows coverage depth a common evaluation point.

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 active identity attacks (credential stuffing, MFA bypass, session token theft, lateral movement using stolen credentials) correlated across authentication and access logs.

Runs all detection and response logic locally on the endpoint without requiring a connection to the vendor cloud, preserving full detection capability in air-gapped networks, regulated environments, and high-latency conditions.

Executes endpoint response actions automatically upon confirmed detection (process termination, file quarantine, registry key removal, and ransomware rollback), without waiting for analyst approval. Scope of automated actions and rollback fidelity are the primary quality differentiators.

Scans endpoints for missing OS and application patches, surfacing vulnerability exposure without requiring a separate vulnerability management scanner.

Detects ransomware encryption activity using behavioral signals and restores affected files from shadow copies or local snapshots. Rollback depth and speed are primary quality metrics.

Executes unknown files in an isolated VM before allowing them to run on the endpoint, enabling detection of zero-day and evasive malware through behavioral observation.

Detects and blocks malware using behavioral analysis and ML models rather than signature matching. Prevents execution of known and novel malware including script-based and fileless attacks.

Blocks exploit techniques at the point of execution (memory injection, process hollowing, credential dumping), independent of whether the exploited application or CVE is known.

Restricts execution to an approved application inventory. Prevents execution of any unapproved binary, script, or library regardless of whether it is recognized as malicious.

Detects and blocks endpoint threats using behavioral analysis of endpoint, application and user activity.

Assesses endpoints for vulnerabilities and misconfigurations and supports built-in or integrated patch and virtual patching.

Scores and ranks correlated incidents by signal severity, asset value, and business impact so analysts work the highest-priority threats first.

Assembles the full attack narrative around an alert (affected assets, related events, process tree, network connections, timeline), without analyst-initiated investigation steps.

Correlates security events across endpoint, network, identity, cloud, and email telemetry in a unified detection engine, detecting multi-stage attacks that span domains and would appear benign in any single-domain view.

Provides a query language and historical telemetry store for analyst-led hunting: differentiation is query expressiveness, cross-domain join capability, and data retention period.