Results for “ransomware-protection”

Hardens Windows Active Directory environments against ransomware by configuring Group Policy Objects with AppLocker rules, Controlled Folder Access, Attack Surface Reduction rules, and lateral movement restrictions.

Executes structured recovery from a ransomware incident following NIST and CISA frameworks, including environment isolation, forensic evidence preservation, clean infrastructure rebuild, prioritized system restoration from verified backups, credential reset, and validation against re-infection.

Designs and implements a ransomware-resilient backup strategy following the 3-2-1-1-0 methodology, including asset classification, immutable storage configuration, credential isolation, and automated restore testing.

Validate ransomware recovery plans by testing backup restore operations, measuring RTO/RPO targets, verifying data integrity, and documenting recovery gaps in an isolated lab environment.

Deploys and monitors ransomware canary files across critical directories using Python's watchdog library for real-time filesystem event detection, triggering alerts via email, Slack, or syslog when decoy files are accessed.

Deploy Runtime Application Self-Protection (RASP) agents to detect and block attacks from within application runtime, covering OpenRASP integration, attack pattern detection, and security policy configuration for Java and Python web applications.

Deploy Mimecast Targeted Threat Protection including URL Protect, Attachment Protect, Impersonation Protect, and Internal Email Protect to defend against advanced phishing and spearphishing attacks.

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

Prevents destructive operations on production systems and restricts file edits to a specific directory when running agents autonomously.

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.