How to Improve Data Security in Zero Trust

🚨2024 Replay: Advancing Zero Trust Maturity Through Visibility & Analytics 🔍 Released by the NSA, this Cybersecurity Information Sheet emphasizes the pivotal role of visibility and analytics in the Zero Trust framework. These principles form a cornerstone of proactive cybersecurity—delivering actionable insights to strengthen detection and response capabilities. Key Takeaways: 📊 Logging: Focus on collecting pertinent activity logs across networks and user systems; indiscriminate data collection isn’t practical. 🛠️ Centralized SIEM: Leverage Security Information and Event Management tools to aggregate and analyze data for enhanced threat detection. 🔐 Risk Analytics: Use dynamic scoring systems enriched by CVEs and real-time vulnerabilities to stay ahead of threats. 🧠 UEBA (User and Entity Behavior Analytics): Harness AI/ML to spot anomalous behaviors that may signal insider threats. 🌐 Threat Intelligence Integration: Enrich internal data with external threat feeds for comprehensive situational awareness. 🚦 Automated Policies: Implement dynamic access controls and configurations to adapt to an evolving threat landscape in real time. 📜 Quote from the CSI: "Detecting and identifying potential threats requires both human and technological elements to understand the entirety of the network, to detect anomalous changes, and to react to an incident expediently and properly." 📅 This post is part of my year-end review of 2024’s most impactful cybersecurity documents. Critical guidance—like this one from May 2024—often fades after its initial promotion. Revisiting these documents allows us to refocus on foundational recommendations for enhancing security postures. 💬 Link to the document in the comments. #cybersecurity #threathunting #analytics #data #visibility #cloudsecurity #technology #informationsecurity #artificialintelligence #zerotrust #computersecurity

When discussing zero trust (ZT), many focus on high-level principles like “never trust, always verify” or enforcing least privilege. But here’s what isn’t often talked about—and cybersecurity practitioners should pay attention to: 1. Map Transaction Flows First Most organizations jump to deploying zero trust policies without understanding their transaction flows. This step involves identifying how data, users, applications, and services interact within your Protect Surface. Use tools like network diagrams, API monitoring, and observability platforms to create detailed maps, revealing hidden dependencies and misconfigurations. 2. Handle Encrypted Traffic Thoughtfully Encryption often obscures the data flow, making it harder to map transactions effectively. Analyze metadata (e.g., packet sizes, timing) or use TLS inspection sparingly. Tools that monitor the application layer (e.g., logging and telemetry) can also provide visibility without breaking encryption. 3. Refine Continuously Zero trust is iterative. Start with broad segmentation (macro) and refine to micro-segmentation as your transaction flow insights improve. Remove unnecessary services and protocols incrementally—after verifying their operational impact—to reduce the attack surface. 4. Use AI/ML to Automate Mapping Traditional transaction mapping methods are manual and error-prone. Modern tools powered by AI/ML (e.g., Cisco Secure Workload or Illumio) dynamically analyze network traffic, identify patterns, and update maps in real time, saving time and improving accuracy. 5. Integrate Security into System Design Use insights from your mapping to implement granular, context-aware policies that account for dynamic conditions like device posture, user behavior, and workload interactions. This approach ensures policies are both practical and flexible. Are you focusing enough on mapping transaction flows in your zero trust strategy? If not, what’s holding you back? Props to Cloud Security Alliance and lead authors Vinotth Ramalingam and Michael Roza for this fantastic research. #ZeroTrust #Cybersecurity #CloudSecurity #Infosec John Kindervag Dr. Chase Cunningham

The 𝗔𝗜 𝗗𝗮𝘁𝗮 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 guidance from 𝗗𝗛𝗦/𝗡𝗦𝗔/𝗙𝗕𝗜 outlines best practices for securing data used in AI systems. Federal CISOs should focus on implementing a comprehensive data security framework that aligns with these recommendations. Below are the suggested steps to take, along with a schedule for implementation. 𝗠𝗮𝗷𝗼𝗿 𝗦𝘁𝗲𝗽𝘀 𝗳𝗼𝗿 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 1. Establish Governance Framework     - Define AI security policies based on DHS/CISA guidance.     - Assign roles for AI data governance and conduct risk assessments.  2. Enhance Data Integrity     - Track data provenance using cryptographically signed logs.     - Verify AI training and operational data sources.     - Implement quantum-resistant digital signatures for authentication.  3. Secure Storage & Transmission     - Apply AES-256 encryption for data security.     - Ensure compliance with NIST FIPS 140-3 standards.     - Implement Zero Trust architecture for access control.  4. Mitigate Data Poisoning Risks     - Require certification from data providers and audit datasets.     - Deploy anomaly detection to identify adversarial threats.  5. Monitor Data Drift & Security Validation     - Establish automated monitoring systems.     - Conduct ongoing AI risk assessments.     - Implement retraining processes to counter data drift.  𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗳𝗼𝗿 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻  Phase 1 (Month 1-3): Governance & Risk Assessment   • Define policies, assign roles, and initiate compliance tracking.   Phase 2 (Month 4-6): Secure Infrastructure   • Deploy encryption and access controls.   • Conduct security audits on AI models. Phase 3 (Month 7-9): Active Threat Monitoring • Implement continuous monitoring for AI data integrity.   • Set up automated alerts for security breaches.   Phase 4 (Month 10-12): Ongoing Assessment & Compliance   • Conduct quarterly audits and risk assessments.   • Validate security effectiveness using industry frameworks.  𝗞𝗲𝘆 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗙𝗮𝗰𝘁𝗼𝗿𝘀   • Collaboration: Align with Federal AI security teams.   • Training: Conduct AI cybersecurity education.   • Incident Response: Develop breach handling protocols.   • Regulatory Compliance: Adapt security measures to evolving policies.