Amplifier Security

Continuously learns from your environment's baseline behavior and network patterns using unsupervised ML models that adapt to legitimate activity, reducing false positives compared to static signature-based detection. The system builds behavioral profiles per endpoint and user, enabling detection of zero-day exploits and novel attack patterns that don't match known signatures. Models retrain incrementally as new data arrives, allowing the system to evolve without manual rule updates.

Executes pre-defined or AI-generated response playbooks automatically when threats are detected, eliminating manual triage delays. The system integrates with endpoint management APIs to execute containment actions (isolate network, kill process, revoke credentials) and coordinates with ticketing systems to create incidents with full context. Response actions are logged with rollback capabilities, allowing security teams to undo automated actions if false positives occur.

Deploys lightweight agents on endpoints that continuously stream process execution, network connection, file system, and registry activity to a centralized backend, normalizing data across Windows, macOS, and Linux into a unified schema. The agent uses kernel-level hooks (ETW on Windows, kprobes on Linux) to capture events with minimal performance overhead (<2% CPU). Telemetry is buffered locally and transmitted in batches to reduce network bandwidth while maintaining real-time alerting capability.

Automatically enriches detected threats with contextual intelligence from multiple sources including internal threat databases, public threat feeds (IP reputation, malware hashes), and OSINT data. The system performs real-time lookups against these sources during alert generation, adding risk scores, known attack campaigns, and remediation recommendations to each alert. Enrichment data is cached locally to reduce latency and API call costs.

Exports threat alerts and telemetry to external security tools via REST APIs, webhooks, and syslog, enabling integration with SIEM platforms (Splunk, ELK, Sentinel), ticketing systems (Jira, ServiceNow), and other security orchestration tools. The system provides pre-built connectors for common platforms and a generic webhook interface for custom integrations. Alert payloads include full context (process tree, network connections, file hashes) to enable downstream analysis without requiring additional data collection.

Automatically generates compliance reports (PCI-DSS, HIPAA, SOC 2) documenting threat detection, response actions, and system monitoring activities. The system maintains immutable audit logs of all detection decisions, remediation actions, and configuration changes, with cryptographic signatures preventing tampering. Reports include executive summaries, detailed threat timelines, and evidence of security controls in operation.

Profiles normal user and service account behavior (login times, accessed resources, privilege escalation patterns) and generates anomaly scores when activity deviates significantly from baseline. The system uses statistical models (isolation forests, autoencoders) to detect insider threats, compromised credentials, and lateral movement by non-human actors. Anomaly scores are combined with threat context to identify high-risk activities like data exfiltration or privilege escalation.

Analyzes network connections from endpoints to identify suspicious communication patterns, command-and-control (C2) callbacks, and lateral movement attempts. The system uses protocol analysis to detect encrypted tunneling (SSH tunnels, DNS tunneling), data exfiltration over unusual channels, and connections to known malicious IP ranges. Detection combines network flow analysis with endpoint process context to attribute traffic to specific applications and users.