Strategies for IT Asset Data Security

𝗗𝗮𝘆 𝟭𝟬: 𝗣𝗿𝗲𝗽𝗮𝗿𝗲𝗱𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 We know the cost of response can be 100 times the cost of prevention, but when unprepared, the consequences are astronomical. A key prevention measure is a 𝗽𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗱𝗲𝗳𝗲𝗻𝘀𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 to anticipate and neutralize threats before they cause harm. Many enterprises struggled during crises like 𝗟𝗼𝗴𝟰𝗷 or 𝗠𝗢𝗩𝗘𝗶𝘁 due to limited visibility into their IT estate. Proactive threat management combines 𝗮𝘀𝘀𝗲𝘁 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆, 𝘁𝗵𝗿𝗲𝗮𝘁 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻, 𝗶𝗻𝗰𝗶𝗱𝗲𝗻𝘁 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲, and 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. Here are few practices to address proactively: 1. 𝗔𝘀𝘀𝗲𝘁 𝗩𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 Having a strong understanding of your assets and dependencies is foundational to security. Maintain 𝗦𝗕𝗢𝗠𝘀 to track software components and vulnerabilities. Use an updated 𝗖𝗠𝗗𝗕 for hardware, software, and cloud assets. 2. 𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗧𝗵𝗿𝗲𝗮𝘁 𝗛𝘂𝗻𝘁𝗶𝗻𝗴 Identify vulnerabilities and threats before escalation. • Leverage 𝗦𝗜𝗘𝗠/𝗫𝗗𝗥 for real-time monitoring and log analysis. • Use AI/ML tools to detect anomalies indicative of lateral movement, insider threat, privilege escalations or unusual traffic. • Regularly hunt for unpatched systems leveraging SBOM and threat intel. 3. 𝗕𝘂𝗴 𝗕𝗼𝘂𝗻𝘁𝘆 𝗮𝗻𝗱 𝗥𝗲𝗱 𝗧𝗲𝗮𝗺𝗶𝗻𝗴 Uncover vulnerabilities before attackers do. • Implement bug bounty programs to identify and remediate exploitable vulnerabilities. • Use red teams to simulate adversary tactics and test defensive responses. • Conduct 𝗽𝘂𝗿𝗽𝗹𝗲 𝘁𝗲𝗮𝗺 exercises to share insights and enhance security controls. 4. 𝗜𝗺𝗺𝘂𝘁𝗮𝗯𝗹𝗲 𝗕𝗮𝗰𝗸𝘂𝗽𝘀 Protect data from ransomware and disruptions with robust backups. • Use immutable storage to prevent tampering (e.g., WORM storage). • Maintain offline immutable backups to guard against ransomware. • Regularly test backup restoration for reliability. 5. 𝗧𝗵𝗿𝗲𝗮𝘁 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 Stay ahead of adversaries with robust intelligence. • Simulate attack techniques based on known adversaries like Scatter Spider • Share intelligence within industry groups like FS-ISAC to track emerging threats. 6. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆-𝗙𝗶𝗿𝘀𝘁 𝗖𝘂𝗹𝘁𝘂𝗿𝗲 Employees are the first line of defense. • Train employees to identify phishing and social engineering. • Adopt a “𝗦𝗲𝗲 𝗦𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴, 𝗦𝗮𝘆 𝗦𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴” approach to foster vigilance. • Provide clear channels for reporting incidents or suspicious activity. Effectively managing 𝗰𝘆𝗯𝗲𝗿 𝗿𝗶𝘀𝗸 requires a 𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝗼𝗳 𝗽𝗲𝘀𝘀𝗶𝗺𝗶𝘀𝗺 𝗮𝗻𝗱 𝘃𝗶𝗴𝗶𝗹𝗮𝗻𝗰𝗲, investment in tools and talent, and alignment with a defense-in-depth strategy. Regular testing, automation, and a culture of continuous improvement are essential to maintaining a strong security posture. #VISA #Cybersecurity #IncidentResponse #PaymentSecurity #12DaysOfCybersecurityChristmas

Everyone’s feeding data into AI engines, but when it leaves secure systems, the guardrails are often gone. Exposure grows, controls can break down, and without good data governance, your organization's most important assets may be at risk. Here's what needs to happen: 1. Have an established set of rules about what’s allowed/not allowed regarding the use of organizational data that is shared organization-wide, not just with the IT organization and the CISO team. 2. Examine the established controls on information from origin to destination and who has access every step of the way: end users, system administrators, and other technology support people. Implement new controls where needed to ensure the proper handling and protection of critical data. You can have great technical controls, but if there are way too many people who have access and who don’t need it for legitimate business or mission purposes, it puts your organization at risk. 3. Keep track of the metadata that is collected and how well it’s protected. Context matters. There’s a whole ecosystem associated with any network activity or data interchange, from emails or audio recordings to bank transfers. There’s the transaction itself and its contents, and then there’s the metadata about the transaction and the systems and networks that it traversed on its way from point A to point B. This metadata can be used by adversaries to engineer successful cyberattacks. 4. Prioritize what must be protected In every business, some data has to be more closely managed than others. At The Walt Disney Company, for example, we heavily protected the dailies (the output of the filming that went on that day) because the IP was worth millions. In government, it was things like planned military operations that needed to be highly guarded. You need an approach that doesn’t put mission-critical protections on what the cafeteria is serving for lunch, or conversely, let a highly valuable transaction go through without a VPN, encryption, and other protections that make it less visible. Takeaway: Data is a precious commodity and one of the most valuable assets an organization can have today. Because the exchange-for-value is potentially so high, bad actors can hold organizations hostage and demand payment simply by threatening to use it.

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.