Introducing pypanther: The Future of Code-Driven Detection and Response
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Transform
cloud noise into
security signal

Transform
cloud noise
into security
signal

Panther provides data-driven security teams the tools they need to create actionable alerts at cloud scale.
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Loved by your favorite data-driven security teams
Features and Benefits
Increase Your Coverage, Not Your Costs.

The Future of Detection and Response Is Code-Driven

Automate, test, and deploy with confidence.
• Code, test, and deploy detection rules in Python for maximum flexibility

• Enable CI/CD for automated deployments of new content

• Tune and update logic across all your detections with simple overrides
Learn More About DaC
Severity:High
LogType:GitHub.Audit
MITRE ATT&CK:TA0001:T1195
ResourceTypes:[AWS.S3.Bucket]
ExpectedResult:True
Tag:Privilege Escalation
RuleID:Snowflake.AccountAdminGranted
LogType:GCP.AuditLog
Severity:Medium
PCI:7.1.2
use cases
Detect and Correlate Threats Across All Your Security Data
Data Exfiltration
Insider Threats
Privilege Escalation
Anomalous Activity Detection
Advanced Persistent Threats (APTs)
Malware and Ransomware Attacks
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Data Exfiltration
Detect large data transfers or unusual data access patterns. Find anomalies in your cloud logs based on size and frequency of data being moved or accessed, especially to/from unusual external IP addresses or at odd hours.
Log sources
Network traffic logs
File access logs
Cloud service logs
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Insider Threats
Detect unauthorized access to sensitive data, unusual after-hours activity, or anomalous data transfers that could indicate malicious insider behavior.
Log sources
Access logs
File activity logs
Email logs
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Privilege Escalation
Identify signs of credential misuse, such as multiple failed login attempts, logins from unusual locations, or access to sensitive resources that are not typically used by a given user.
Log sources
Authentication logs
VPN logs
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Anomalous Activity Detection
Flag deviations from a baseline of expected normal behavior. This can include unusual access patterns, file movements, or network traffic.
Log sources
Network logs
Application logs
User behavior logs
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Advanced Persistent Threats
Employ complex correlation rules and heuristic analyses to identify low-and-slow attack patterns, lateral movement, and other stealthy behaviors typical of APTs.
Log sources
Network logs
Endpoint detection and response (EDR) logs
Server logs
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Malware and Ransomware Attacks
Detect signatures of known malware and ransomware, as well as behavioral indicators such as mass file encryption or changes to registry keys.
Log sources
Antivirus logs
File system logs
Registry logs

Detect large data transfers or unusual data access patterns. Find anomalies in your cloud logs based on size and frequency of data being moved or accessed, especially to/from unusual external IP addresses or at odd hours.
Log sources
Network traffic logs
File access logs
Cloud service logs

Detect unauthorized access to sensitive data, unusual after-hours activity, or anomalous data transfers that could indicate malicious insider behavior.
Log sources
Access logs
File activity logs
Email logs

Identify signs of credential misuse, such as multiple failed login attempts, logins from unusual locations, or access to sensitive resources that are not typically used by a given user.
Log sources
Authentication logs
VPN logs

Flag deviations from a baseline of expected normal behavior. This can include unusual access patterns, file movements, or network traffic.
Log sources
Network logs
Application logs
User behavior logs

Employ complex correlation rules and heuristic analyses to identify low-and-slow attack patterns, lateral movement, and other stealthy behaviors typical of APTs.
Log sources
Network logs
Endpoint detection and response (EDR) logs
Server logs

Detect signatures of known malware and ransomware, as well as behavioral indicators such as mass file encryption or changes to registry keys.
Log sources
Antivirus logs
File system logs
Registry logs
Escape Cloud Noise. Detect Security Signal.
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