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.
EXPERT / Digital Line
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Contributes Digital Line editorial materials on data, integration, enterprise platforms and practical AI use.
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.
A practical approach to overcoming SOC alert fatigue through MITRE ATT&CK mapping, SOAR automation, and NIST CSF 2.0 business prioritization.
The Miasma attack on Red Hat's npm packages highlights supply chain risks and credential theft, which are critical for AI integration security.
Protecting AI services from prompt injection and data leaks in 2026 requires a comprehensive approach and integration with existing cybersecurity tools.
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.
The rise of hybrid and remote work models significantly complicates the task of protecting corporate content. By 2026, effective DLP strategies must adapt to these changes, focusing on preventative measures and automation.
Approximately 60% of corporate data breaches in 2025 were recorded after an employee's termination, while their access formally remained active. Insider threats continue to be one of the most complex cybersecurity challenges.