{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_amplifier-security","slug":"amplifier-security","name":"Amplifier Security","type":"product","url":"https://www.amplifiersecurity.com","page_url":"https://unfragile.ai/amplifier-security","categories":["code-review-security"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_amplifier-security__cap_0","uri":"capability://safety.moderation.adaptive.machine.learning.based.threat.detection","name":"adaptive machine learning-based threat detection","description":"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.","intents":["detect novel and zero-day threats without waiting for signature updates","reduce false positive alerts that waste security team time on non-threats","automatically adapt detection models to my specific environment's normal behavior","catch advanced persistent threats that use obfuscation or living-off-the-land techniques"],"best_for":["mid-sized companies with 100-5000 endpoints lacking dedicated ML security expertise","organizations with distributed teams needing detection that doesn't rely on centralized rule management","teams migrating from signature-only detection to behavioral threat detection"],"limitations":["ML model internals are proprietary and not transparent — cannot audit decision logic or training data composition","requires 2-4 weeks of baseline learning period before detection accuracy reaches optimal levels","false negative rates for sophisticated attacks not disclosed publicly, making ROI comparison difficult","model retraining latency may cause 6-12 hour delays in adapting to new attack patterns during active incidents"],"requires":["Windows, macOS, or Linux endpoints with agent installation capability","network connectivity to Amplifier cloud backend for model updates (can operate in hybrid mode)","minimum 100 endpoints to generate sufficient behavioral data for effective model training","API credentials for integration with SIEM or ticketing systems (optional but recommended)"],"input_types":["endpoint telemetry (process execution, network connections, file operations)","user behavior logs (login patterns, privilege escalation attempts)","network traffic metadata (DNS queries, connection destinations, protocol anomalies)"],"output_types":["threat severity scores (0-100 scale)","behavioral anomaly classifications (process injection, lateral movement, data exfiltration)","structured threat alerts (JSON/syslog format for SIEM ingestion)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_amplifier-security__cap_1","uri":"capability://automation.workflow.automated.incident.response.and.remediation.orchestration","name":"automated incident response and remediation orchestration","description":"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.","intents":["automatically isolate compromised endpoints to prevent lateral movement without waiting for manual approval","execute containment actions (kill malicious processes, block network access) in seconds rather than hours","reduce mean time to remediation (MTTR) by eliminating manual alert triage and approval workflows","create audit trails of all automated actions for compliance and post-incident analysis"],"best_for":["teams with limited security operations staff (1-3 analysts) needing 24/7 response capability","organizations with strict SLAs requiring sub-5-minute incident response times","companies operating in regulated industries (healthcare, finance) requiring documented incident response procedures"],"limitations":["automated isolation actions are irreversible for 30+ minutes, risking business disruption if false positives occur","playbook customization requires manual JSON/YAML editing — no visual workflow builder provided","integration with third-party endpoint management tools (Intune, Jamf) requires custom API connectors not pre-built","no rollback capability for destructive actions like credential revocation — requires manual restoration"],"requires":["API credentials for endpoint management platform (Windows SCCM, Jamf, Intune, or custom)","integration with ticketing system (Jira, ServiceNow, or webhook endpoint)","network connectivity from Amplifier cloud to internal management APIs (or on-premises agent deployment)","security team approval workflow defined before enabling automated isolation actions"],"input_types":["threat detection alerts (from adaptive ML detection capability)","playbook definitions (JSON/YAML with action sequences)","endpoint inventory and API credentials"],"output_types":["incident tickets with full threat context and remediation actions taken","endpoint isolation confirmations and network quarantine status","audit logs of all automated actions with timestamps and rollback records"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_amplifier-security__cap_2","uri":"capability://data.processing.analysis.continuous.endpoint.telemetry.collection.and.normalization","name":"continuous endpoint telemetry collection and normalization","description":"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.","intents":["collect comprehensive endpoint activity data without impacting endpoint performance","normalize telemetry from mixed Windows/macOS/Linux environments into a single queryable format","enable forensic investigation by retaining 30+ days of detailed activity logs per endpoint","feed behavioral ML models with high-fidelity data for accurate anomaly detection"],"best_for":["organizations with heterogeneous endpoint environments (Windows, macOS, Linux)","teams needing forensic-grade activity logs for incident investigation and compliance audits","companies with performance-sensitive environments (trading floors, rendering farms) requiring <2% CPU overhead"],"limitations":["agent installation requires local administrator/root privileges, blocking deployment on locked-down endpoints","kernel-level telemetry collection may trigger false positives in antivirus/EDR tools on some systems","data retention is cloud-only — no local caching option for air-gapped networks or high-latency connections","agent updates require endpoint restart on Windows, causing brief connectivity gaps during rollout"],"requires":["Windows 10+ (with ETW support), macOS 10.14+, or Linux kernel 4.4+ (with kprobes/eBPF)","local administrator or root access for agent installation","minimum 500 MB disk space per endpoint for local telemetry buffering","network connectivity to Amplifier cloud backend (or on-premises collector with hybrid deployment)"],"input_types":["kernel-level system events (process creation, network connections, file operations)","registry/configuration changes (Windows)","user authentication and privilege escalation events"],"output_types":["normalized telemetry events (JSON format with standardized field names)","time-series activity streams queryable by endpoint, user, or process","forensic-grade activity logs with 30+ day retention"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_amplifier-security__cap_3","uri":"capability://memory.knowledge.threat.intelligence.integration.and.enrichment","name":"threat intelligence integration and enrichment","description":"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.","intents":["automatically correlate detected threats with known attack campaigns and threat actors","add reputation scores and context to raw alerts to help analysts prioritize investigation","identify if detected malware or IPs are part of known botnets or ransomware campaigns","provide actionable remediation recommendations based on threat intelligence"],"best_for":["security teams lacking in-house threat intelligence expertise","organizations needing to correlate internal detections with external threat context","teams operating in regulated industries requiring documented threat attribution"],"limitations":["threat intelligence feeds are third-party and may have 6-24 hour delays in detecting new threats","enrichment accuracy depends on quality of external feeds — no validation of feed accuracy provided","API rate limits on threat intelligence sources may cause enrichment delays during high-volume attack periods","no capability to ingest custom internal threat intelligence or proprietary indicators of compromise (IOCs)"],"requires":["API credentials for threat intelligence providers (VirusTotal, AlienVault OTX, etc.)","network connectivity to external threat intelligence APIs","optional: custom threat feed endpoint for internal IOC integration"],"input_types":["detected threat indicators (IP addresses, file hashes, domain names, process signatures)","external threat intelligence feeds (JSON/CSV format)"],"output_types":["enriched threat alerts with reputation scores, campaign attribution, and threat actor profiles","remediation recommendations linked to threat intelligence","correlation data showing relationships between detected threats and known campaigns"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_amplifier-security__cap_4","uri":"capability://tool.use.integration.siem.and.security.tool.integration.via.standardized.apis","name":"siem and security tool integration via standardized apis","description":"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.","intents":["send threat alerts to our existing SIEM for centralized logging and correlation","automatically create incidents in our ticketing system with full threat context","integrate with our security orchestration platform (SOAR) for automated response workflows","export telemetry for long-term forensic analysis and compliance reporting"],"best_for":["organizations with existing SIEM/SOAR investments needing to integrate new detection capabilities","teams using multiple security tools requiring a central hub for alert aggregation","companies with strict data residency requirements needing on-premises SIEM integration"],"limitations":["pre-built connectors only available for top 10 SIEM/ticketing platforms — custom integrations require webhook development","alert payload schema is fixed — no field customization available for downstream tool requirements","webhook delivery is not guaranteed if Amplifier backend experiences outages — no persistent queue for failed deliveries","API rate limits may cause alert delays during high-volume attack periods (>1000 alerts/minute)"],"requires":["REST API endpoint or webhook URL for target integration platform","API credentials or authentication tokens for target systems","network connectivity from Amplifier cloud to internal SIEM/ticketing systems (or VPN tunnel for air-gapped networks)"],"input_types":["threat detection alerts from ML detection engine","endpoint telemetry and forensic data","incident response actions and remediation status"],"output_types":["JSON alert payloads with full threat context","syslog-formatted events for SIEM ingestion","webhook POST requests to custom endpoints","structured incident tickets with threat details and remediation recommendations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_amplifier-security__cap_5","uri":"capability://data.processing.analysis.compliance.reporting.and.audit.trail.generation","name":"compliance reporting and audit trail generation","description":"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.","intents":["generate compliance reports for auditors without manual data collection","prove to regulators that we have continuous threat monitoring and incident response capabilities","document all security incidents and remediation actions for post-incident reviews","maintain audit trails showing who accessed what data and when for forensic investigations"],"best_for":["organizations in regulated industries (healthcare, finance, government) requiring compliance documentation","companies undergoing security audits or certifications (SOC 2, ISO 27001, PCI-DSS)","teams needing to demonstrate security controls to customers or insurance providers"],"limitations":["compliance report templates are pre-defined — no customization for industry-specific requirements","audit logs are stored in Amplifier cloud only — no option for on-premises audit log storage for air-gapped networks","report generation requires manual scheduling — no automated monthly/quarterly report delivery","compliance reports do not include risk assessments or remediation recommendations, requiring manual analysis"],"requires":["compliance framework selection (PCI-DSS, HIPAA, SOC 2, ISO 27001)","minimum 90 days of telemetry and alert data for meaningful compliance reports","optional: custom report template configuration via API"],"input_types":["threat detection alerts and remediation actions","endpoint telemetry and activity logs","system configuration and policy changes"],"output_types":["PDF compliance reports with executive summaries and detailed evidence","CSV exports of threat timelines and remediation actions","JSON audit logs with cryptographic signatures for tamper-proof documentation"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_amplifier-security__cap_6","uri":"capability://data.processing.analysis.user.and.entity.behavior.analytics.ueba.with.anomaly.scoring","name":"user and entity behavior analytics (ueba) with anomaly scoring","description":"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.","intents":["detect compromised user accounts by identifying unusual login patterns or resource access","identify insider threats by detecting abnormal data access or privilege escalation attempts","catch lateral movement by detecting when service accounts access resources outside their normal scope","reduce false positives by correlating user behavior anomalies with other threat indicators"],"best_for":["organizations with large user bases (1000+) requiring automated insider threat detection","teams needing to detect compromised credentials without relying on password breach databases","companies with complex permission structures requiring behavioral validation of access patterns"],"limitations":["UEBA models require 4-8 weeks of baseline learning before generating reliable anomaly scores","seasonal variations in user behavior (vacation periods, project cycles) may cause false positives","service account behavior is difficult to profile — requires manual whitelisting of legitimate automation","no capability to correlate UEBA anomalies with external threat intelligence for context"],"requires":["Active Directory or identity provider integration for user and group information","minimum 30 days of user activity logs for baseline model training","network connectivity to Amplifier backend for model updates and anomaly scoring"],"input_types":["user authentication logs (login times, locations, devices)","resource access logs (file shares, databases, applications)","privilege escalation events and sudo/UAC logs"],"output_types":["anomaly scores (0-100 scale) per user and activity type","behavioral deviation alerts with context (normal vs. observed activity)","risk scores combining UEBA anomalies with other threat indicators"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_amplifier-security__cap_7","uri":"capability://data.processing.analysis.network.traffic.analysis.and.lateral.movement.detection","name":"network traffic analysis and lateral movement detection","description":"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.","intents":["detect command-and-control communications from compromised endpoints","identify data exfiltration attempts by detecting unusual outbound connections and data volumes","catch lateral movement by detecting when endpoints communicate with internal systems outside normal patterns","block malware callbacks by identifying connections to known malicious IP ranges"],"best_for":["organizations with complex internal networks requiring lateral movement detection","teams needing to detect data exfiltration without deploying network DLP solutions","companies with strict egress filtering policies requiring validation of outbound connections"],"limitations":["encrypted traffic (HTTPS, TLS) cannot be inspected without MITM proxies — detection limited to IP/port analysis","legitimate cloud services (AWS, Azure, Office 365) generate high volumes of traffic to shared IP ranges, causing false positives","DNS tunneling detection requires DNS query logging — not available if DNS is handled by external providers","lateral movement detection requires baseline learning of internal network topology — 2-4 weeks required for accuracy"],"requires":["network flow data from endpoints (NetFlow, sFlow, or packet capture)","optional: DNS query logs for DNS tunneling detection","optional: proxy logs for encrypted traffic analysis"],"input_types":["network flow data (source/destination IP, port, protocol, bytes transferred)","DNS query logs (domain names, query types, response codes)","endpoint process context (which application initiated the connection)"],"output_types":["network anomaly alerts with C2 callback indicators","lateral movement detection alerts with source/destination context","data exfiltration warnings with volume and destination analysis"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Windows, macOS, or Linux endpoints with agent installation capability","network connectivity to Amplifier cloud backend for model updates (can operate in hybrid mode)","minimum 100 endpoints to generate sufficient behavioral data for effective model training","API credentials for integration with SIEM or ticketing systems (optional but recommended)","API credentials for endpoint management platform (Windows SCCM, Jamf, Intune, or custom)","integration with ticketing system (Jira, ServiceNow, or webhook endpoint)","network connectivity from Amplifier cloud to internal management APIs (or on-premises agent deployment)","security team approval workflow defined before enabling automated isolation actions","Windows 10+ (with ETW support), macOS 10.14+, or Linux kernel 4.4+ (with kprobes/eBPF)","local administrator or root access for agent installation"],"failure_modes":["ML model internals are proprietary and not transparent — cannot audit decision logic or training data composition","requires 2-4 weeks of baseline learning period before detection accuracy reaches optimal levels","false negative rates for sophisticated attacks not disclosed publicly, making ROI comparison difficult","model retraining latency may cause 6-12 hour delays in adapting to new attack patterns during active incidents","automated isolation actions are irreversible for 30+ minutes, risking business disruption if false positives occur","playbook customization requires manual JSON/YAML editing — no visual workflow builder provided","integration with third-party endpoint management tools (Intune, Jamf) requires custom API connectors not pre-built","no rollback capability for destructive actions like credential revocation — requires manual restoration","agent installation requires local administrator/root privileges, blocking deployment on locked-down endpoints","kernel-level telemetry collection may trigger false positives in antivirus/EDR tools on some systems","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.133Z","last_scraped_at":"2026-04-05T13:23:42.561Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=amplifier-security","compare_url":"https://unfragile.ai/compare?artifact=amplifier-security"}},"signature":"IbK49cAl/J++nBdwNXym00aeYkm4/3yGeRX7kHx+z0XJJJPB3B44xgK/ZkflrqpV/UV5LgojmPHLb2X21PGCDw==","signedAt":"2026-06-20T14:38:51.858Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/amplifier-security","artifact":"https://unfragile.ai/amplifier-security","verify":"https://unfragile.ai/api/v1/verify?slug=amplifier-security","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}