Results for “ransomwatch”

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.

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.

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.

Implements immutable backup strategy using restic with S3-compatible storage and object lock for ransomware-resistant data protection, automating backup creation, integrity verification, snapshot retention, and restore testing.

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.

Monitors a deployed URL for regressions after releases by checking HTTP status, console errors, network failures, performance metrics, content integrity, API health, static assets, and SSE streams.

Run OpenClaw remote validation on Linux, macOS, or Windows via the Crabbox or Testbox wrapper, reporting the actual provider and lease id.

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.

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.

Analyze AWS GuardDuty findings, detectors, suppression rules, and member accounts with parallel execution and anti-hallucination guardrails.