Trust in telemetry for cybersecurity decisions

For decades, cybersecurity has relied on the CIA Triad: Confidentiality, Integrity, and Availability. In the era of AI-driven Data Attacks (AIDA), the absence of Trust and mechanisms like TSTL doesn’t just expose traditional infrastructure, it also leaves large language models (LLMs) open to grooming and prompt injection. This means that even defensive AI systems can be compromised if they operate on untrusted data. Encryption still works, but it doesn’t guarantee trust. Once data is decrypted, legacy systems assume it’s safe. AIDA exploits that gap, inferring patterns from metadata, timing, and telemetry to compromise systems without ever “breaking” the math. That’s why Trust must become the fourth pillar of cybersecurity. Not as an abstract idea, but as a cryptographically enforced property that persists across the lifecycle of data, even post-decryption. In my new article, I outline how we must evolve from CIA to CIAT and operationalize Trust through Telemetry-Sealed Trust Layers (TSTL). This is how we shrink attack surfaces, defend against inference, and future-proof enterprise and government infrastructure. #CyberSecurity #AI #AIDA #CIAT #DataTrust #ZeroTrust #Encryption #XSOC #QuantumSafe #Infosec

For SOCs, it’s not just the hackers that pose a threat - it’s the avalanche of data that buries real signals under noise. Security logs, once the fuel for detection, are now both an asset and a liability. The flood of redundant, misaligned, or uncurated telemetry drains not just budgets - but analysts. The challenge isn’t just collecting data - it’s collecting the right data, in the right shape, at the right time. Security tools generate logs by the terabyte. Yet most organizations lack a strategy to qualify, contextualize, or prioritize what enters their SIEMs. As a result: ▪ Real threats get buried in noise. ▪ False positives clutter dashboards, wasting attention. ▪ Costs balloon from excessive licensing and storage. To move from reactive firefighting to proactive defense, SOCs must elevate telemetry management as a core security function. Here's how leading teams do it: 1. Precision Filtering, Not Blanket Collection Start with a threat-informed view: what data truly supports detections? Eliminate noise - e.g., suppress successful login logs unless from unusual geographies or times. 2. Normalization and Enrichment as Multipliers Standardize formats and enrich with business context - asset criticality, user identity, threat intel, geolocation. This transforms raw logs into events that trigger rules more accurately and reduce triage ambiguity. 3. Retention That Reflects Risk Abandon “store everything” habits. Align retention with risk: real-time detection data stays hot; compliance data can go cold. 4. Use Case-Driven Collection Let strategy guide ingestion. Data should map to real correlation rules, MITRE ATT&CK coverage, or compliance needs. If it doesn’t, reconsider ingesting it. Log optimization isn’t just about saving money, it enables: ▪ Faster decision-making ▪ Reduced alert fatigue ▪ Stronger detection fidelity When telemetry pipelines are treated with the same rigor as detection logic or incident response, the SOC becomes sharper and more effective. Final thought…. Data isn't your greatest asset - useful data is. 👉Ask Yourself Are you collecting data to feel secure - or to be secure? #CyberSecurity #SOC #SecOps #ThreatDetection #Telemetry #DataStrategy #DataQuality #OptimizeLogs #LogReduction #SecurityEfficiency #SIEMOptimization #AlertFatigue #TelemetryPipeline