AI in cybersecurity 2027: from perimeter defense to semantic DLP filters
How to protect corporate LLM integrations from data leaks and new attack vectors using NIST AI RMF, MITRE ATT&CK frameworks, and Security by Design architecture.
AUTHOR / Digital Line
Oleh Yermolenko
Editorial contributor
About
Contributes Digital Line editorial materials on data, integration, enterprise platforms and practical AI use.
Published articles17Explore articles
Author’s publications
Current articles · 17Security assessment and roadmap using NIST CSF 2.0
How to transition from chaotic protection to systemic resilience, identify hidden gaps, and implement targeted solutions that limit the blast radius without replacing the entire IT infrastructure.
Building noise-free security monitoring with SIEM, SOC, and SOAR
A practical approach to overcoming SOC alert fatigue through MITRE ATT&CK mapping, SOAR automation, and NIST CSF 2.0 business prioritization.
Zero Trust for legacy systems: protection architecture via IAP and microsegmentation
How to protect legacy infrastructure from unauthorized access and lateral movement without major code refactoring using Identity-Aware Proxy and microsegmentation.
Cybersecurity as a discipline: threat modeling, architecture, and business risks
Moving from chaotic software procurement to systematic cyber resilience through threat modeling, architectural controls, and Security by Design principles.
Zero Trust for legacy ERP without rewriting: IAP, PAM and microsegmentation
How to secure legacy ERP systems without rewriting code. Implement Zero Trust using IAP, PAM, and microsegmentation to mitigate access risks effectively.
Managing mass URL injection and SEO threats
A sudden surge in indexed URLs may signal a security breach or a technical configuration issue. Learn how to localize threats and protect SEO without hasty actions.
Assessing infrastructure readiness for cyberattacks: beyond compliance
Building real enterprise cyber resilience in the NIS2 era, avoiding paper audit traps, and protecting infrastructure through Security by Design.
Supply chain security lessons for AI integrations
The Miasma attack on Red Hat's npm packages highlights supply chain risks and credential theft, which are critical for AI integration security.
AI security platform: protecting AI services from prompt injection and data leaks
Protecting AI services from prompt injection and data leaks in 2026 requires a comprehensive approach and integration with existing cybersecurity tools.
AI drives compliance audit automation
AI-driven compliance is transforming ISO/IEC 27001 and SOC 2 audits by automating evidence collection and non-compliance detection.
DLP for AI Systems: The New Cybersecurity Reality of 2026
By 2026, over 70% of new AI systems will experience data breaches due to inadequate Data Loss Prevention (DLP) integration, necessitating a reevaluation of sensitive information protection in the age of artificial intelligence.