Data-Driven Strategies for Cybersecurity Professionals

The latest joint cybersecurity guidance from the NSA, CISA, FBI, and international partners outlines critical best practices for securing data used to train and operate AI systems recognizing data integrity as foundational to AI reliability. Key highlights include: • Mapping data-specific risks across all 6 NIST AI lifecycle stages: Plan and Design, Collect and Process, Build and Use, Verify and Validate, Deploy and Use, Operate and Monitor • Identifying three core AI data risks: poisoned data, compromised supply chain, and data drift for each with tailored mitigations • Outlining 10 concrete data security practices, including digital signatures, trusted computing, encryption with AES 256, and secure provenance tracking • Exposing real-world poisoning techniques like split-view attacks (costing as little as 60 dollars) and frontrunning poisoning against Wikipedia snapshots • Emphasizing cryptographically signed, append-only datasets and certification requirements for foundation model providers • Recommending anomaly detection, deduplication, differential privacy, and federated learning to combat adversarial and duplicate data threats • Integrating risk frameworks including NIST AI RMF, FIPS 204 and 205, and Zero Trust architecture for continuous protection Who should take note: • Developers and MLOps teams curating datasets, fine-tuning models, or building data pipelines • CISOs, data owners, and AI risk officers assessing third-party model integrity • Leaders in national security, healthcare, and finance tasked with AI assurance and governance • Policymakers shaping standards for secure, resilient AI deployment Noteworthy aspects: • Mitigations tailored to curated, collected, and web-crawled datasets and each with unique attack vectors and remediation strategies • Concrete protections against adversarial machine learning threats including model inversion and statistical bias • Emphasis on human-in-the-loop testing, secure model retraining, and auditability to maintain trust over time Actionable step: Build data-centric security into every phase of your AI lifecycle by following the 10 best practices, conducting ongoing assessments, and enforcing cryptographic protections. Consideration: AI security does not start at the model but rather it starts at the dataset. If you are not securing your data pipeline, you are not securing your AI.

Is your security team stuck in firefighting mode? Use this Cybersecurity Strategy Matrix to build a balanced security roadmap: 𝟭. 𝗘𝗺𝗯𝗲𝗱𝗱𝗲𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 (Existing Systems + Existing Controls) → Strengthen password policies and access management → Enhance patch management processes → Conduct deeper security awareness training → Low risk, focuses on security fundamentals 𝗢𝘂𝘁𝗰𝗼𝗺𝗲: Strong foundation with minimal disruption 𝟮. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 (Existing Systems + New Controls) → Implement EDR/XDR solutions over traditional antivirus → Deploy AI-based threat hunting capabilities → Adopt zero-trust architecture frameworks → Moderate risk, leverages advanced protections 𝗢𝘂𝘁𝗰𝗼𝗺𝗲: Significantly improved protection without system overhaul 𝟯. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗘𝘅𝗽𝗮𝗻𝘀𝗶𝗼𝗻 (New Systems + Existing Controls) → Extend current security monitoring to cloud workloads → Apply existing controls to newly acquired systems (M&A) → Secure shadow IT with established security baselines → Moderate risk, focuses on consistent security coverage 𝗢𝘂𝘁𝗰𝗼𝗺𝗲: Unified security posture across your growing environment 𝟰. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 (New Systems + New Controls) → Build security for containerized environments → Implement quantum-resistant encryption → Develop custom security for IoT/OT environments → Highest risk, prepares for emerging threat landscapes 𝗢𝘂𝘁𝗰𝗼𝗺𝗲: Future-proofed security ready for emerging threats Effective cybersecurity requires balancing immediate needs with long-term resilience. Where is your security program investing today?

As digital footprints expand and cyber threats become more sophisticated, organizations must adopt robust security data pipelines to ensure they are well-equipped to identify, understand, and mitigate risks effectively. A strong data foundation is not just beneficial for cybersecurity at scale; it's essential to ensure these downstream security platforms have the performant underlying queries to give the visibility required. The goal is to create a seamless flow of information that is both actionable and comprehensive, enabling security teams to react swiftly and decisively. 👉 Comprehensive Visibility: At its core, cybersecurity is about visibility. Without a complete view of what's happening across all systems and networks, security teams are blind to the actions of potential threat actors. A strong data foundation built through well-designed security data pipelines ensures that all relevant data is captured, normalized, and made readily available for analysis. This visibility is crucial for detecting correlated signs of compromise that could otherwise go unnoticed until it’s too late. 👉 Scalability: Cybersecurity threats evolve rapidly, and so too must the defenses. Security data pipelines facilitate scalability by automating data ingestion and analysis. As data volumes grow, these pipelines ensure data gets where it needs to go, in the format it needs to be, processing vast quantities of information efficiently. This scalability ensures that security measures can keep pace with expanding network perimeters and increasingly sophisticated attacks. 👉 Speed and Precision in Threat Detection and Response: In cybersecurity, speed is of the essence. The faster a potential threat can be identified and mitigated, the less damage it can do. Security data pipelines accelerate the detection process by leveraging advanced analytics, machine learning, and artificial intelligence to sift through mountains of data in real-time. They enable precise threat detection by correlating disparate data points, highlighting anomalies, and suggesting actionable insights. 👉 Regulatory Compliance and Risk Management: With increasing regulatory demands around data privacy and security, organizations must ensure they have robust mechanisms in place to protect sensitive information. A strong data foundation allows for the enforcement of compliance policies automatically. Being able to securely and efficiently get all your data to S3 or equivalent object storage, then rehydrate that data into SIEMs as needed, is extremely valuable. #otel #ocsf #securitypipelines #telemetrypipelines #siem Edge Delta #cybersecurity #security #splunk #crowdstrike #sentinel

Security operations hasn’t really changed: more alerts, more tools, same burnout. Rather than solving this with the same methodology or by replacing a software, what if we start fresh -- using data and AI? In this week's episode, Saleem Javed Mohamed Ismail and I share how Human Managed is approaching a 20+ year old problem differently: 🔷 Make data the main player. We aren't just referring to protecting the data, but data is HOW you protect the data as well. The actions, policies, and playbooks themselves are data. When we collect and structure that data, AI can use it to advise us and accelerate containment. 🔷 Front-load context with AI. Before a human ever sees an alert, analysis and enrichment are done at the data layer -- so decisions are faster, more consistent, and grounded in full context. 🔷 End-to-end accountability. From detection → understanding → explanation → resolution. Not just dashboards, but direction. 🔷 Build knowledge that compounds. A live, always-up-to-date asset catalog ensures every new insight strengthens the next model, use case, or risk posture. The impact we care about: • Reduced exposure time • Reduced time to respond • Less manual toil, fewer errors • Knowledge that grows with every action The race in cybersecurity isn’t just attacker vs. defender -- it’s speed and consistency vs. noise. 👇link to the full episode below 🛎️ like & subscribe to stay in the loop!

Most data exposure happens in everyday tools: chat, email, file shares, personal devices, and unsanctioned apps, including AI. Keep data where it belongs and reduce the chances it can leak in the first place. This is a business risk, not just an IT issue. Leaders need to care enough to identify the gaps and work with IT to close them properly, with zero trust principles. ☞ Start here: • Map the flow of sensitive data across your apps and work paths. • Establish DLP & Zero Trust to keep files in approved locations and block risky data leakage. • Coach people inside the tools they already use so your cyber strategy supports productivity. If you find that you need to centralize detection and close the gaps across SaaS, email, and endpoints, consider Nightfall AI: https://lnkd.in/ghDW_Rgf #Cybersecurity #DataProtection #SMB #Leadership #DLP #RiskManagement #JasonMakevich