Overview of Digital Forensics and Zero Trust Architecture

Digital Forensics

Investigation of digital crimes to answer questions like who, what, when, where, why, and how.

Acquisition

Collect evidence while preserving its integrity.

Presentation

Report findings in simple terms for court or stakeholders.

Chain of Custody

Documentation of evidence handling to ensure its integrity during an investigation.

File Carving

Reassembling files based on their format and signature when metadata is unavailable.

Anti-Forensics Techniques

Methods used to hide or destroy evidence, such as file deletion, encryption, or steganography.

Zero Trust Architecture (ZTA)

A security model that assumes no user, device, or network is trusted by default. Verification is required for all access.

Core Principles of Zero Trust

Assume breach. Verify explicitly (identity, device, context). Implement least-privilege access.

Recovery

Extract data from evidence.

Analysis

Reconstruct events or identify contraband.

Microsegmentation

Dividing a network into isolated segments to limit the movement of attackers.

ZTA Deployment Models

Resource-Based: Secures individual resources. Enclave-Based: Groups similar resources into enclaves. Cloud-Routed: Secures cloud access.

Key ZTA Components

Policy Engine: Decides access based on policies. Policy Administrator: Enforces decisions. Policy Enforcement Point (PEP): The gatekeeper controlling resource access.

DevSecOps

A practice integrating development, security, and operations to deliver secure software faster.

Phases of the DevSecOps Lifecycle

Plan, Develop, Build, Test, Release, Deliver, Deploy, Operate, Monitor, Feedback.

Key Benefits of DevSecOps

Faster deployments. Reduced failure rates. Baked-in cybersecurity.

CI/CD in DevSecOps

Continuous Integration and Continuous Deployment to automate and streamline the software lifecycle.

Applications of AI in Cybersecurity

Threat detection through anomaly detection. Behavioral analysis of users and devices. Adaptive security that evolves with threats.

Adversarial Machine Learning

The use of AI to trick machine learning models, often used in attacks like deepfakes or AI-powered malware.

Challenges in Digital Forensics

Managing vast data volumes. Detecting anti-forensic techniques. Interpreting artifacts from evolving technologies.

Zero Trust Challenges

Insider threats. Subversion of decision-making processes. Balancing usability and strict access controls.

Key Takeaways in Digital Forensics

Every interaction leaves a digital trail. Recovery techniques may produce incomplete or extraneous data. Continuous learning is required to keep up with technological advancements.

Key Takeaways in ZTA

Trust is dynamic and context-driven. Focus on securing resources, not the network perimeter. Policies must be enforced uniformly across all access points.