Deep-dive technical papers on continuous forensic readiness, AI-powered security operations, graph-based investigation, and the future of modern DFIR practice.
Each paper is written by the Praeventra research team and peer-reviewed for technical accuracy. Free to download, no registration required.
This paper defines the continuous forensic readiness model, contrasts it with traditional reactive collection approaches, and provides a framework for evaluating an organization's current forensic posture. Includes coverage of evidence volatility, collection architecture, and chain-of-custody requirements.
Explores how machine learning and AI-driven correlation are reshaping the SOC analyst workflow. Covers anomaly detection techniques, behavioral baselining, false-positive reduction strategies, and the role of AI in accelerating mean time to understand across complex multi-stage incidents.
A technical examination of how graph databases and relationship modeling change the way analysts reconstruct attacker activity. Covers entity relationship modeling, lateral movement tracing, privilege escalation path analysis, and how graph traversal shortens investigation time in complex, multi-host incidents.
Addresses the forensic data collection challenges unique to zero-trust network architectures, cloud-hybrid environments, and remote work infrastructure. Covers agent deployment strategies, encrypted telemetry pipelines, identity-aware collection controls, and maintaining forensic integrity without perimeter-based trust assumptions.
Explore our technical documentation, API references, and user guides, or speak with our team to discuss how Praeventra fits your security operations environment.
