AI risk management in critical infrastructure via NIST AI RMF 1.0
How critical infrastructure operators can adapt the NIST AI RMF 1.0 framework to defend against specific AI threats and integrate it into corporate cybersecurity.
INSIGHTS & PRACTICE
Intecracy Group
Practical guides to business-process automation: from the first assessment to measurable results.
43 publications
IAM and PAM: Stopping Privilege Creep and Ensuring Secure Off-boarding
How to build a controlled identity lifecycle, implement regular access reviews, and eliminate "orphaned" access vulnerabilities to protect against cyber threats.
OT/IIoT cybersecurity: Protecting critical infrastructure for NIS2
How to secure industrial legacy systems and meet NIS2 requirements without disrupting production processes.
Predictive maintenance in industrial IoT: data pipelines
Data architecture for Predictive Maintenance: how to standardize data flow via OPC UA and distribute computation between Edge and Cloud without replacing SCADA.
AI cost forecasting and FinOps automation: the future of cost management by 2027
Shifting from reactive invoice analysis to proactive cost modeling during architectural design using unit economics and AI assistants.
Cloud-native architecture and cost: how design impacts TCO
Why cloud bills rise after migration and how to embed financial efficiency (FinOps) into infrastructure design during the application planning stage.
Cloud overspending: how to curb over-provisioning and zombie resources
Analyzing cloud architectural errors: from over-provisioning to uncontrolled scaling, and exploring practical mitigation methods through FinOps and proper governance.
Multi-cloud and hybrid infrastructure: when it makes sense
Analyzing a pragmatic approach to multi-cloud and hybrid architectures: how to avoid operational complexity traps, implement FinOps, and secure the organizational perimeter.
FinOps in enterprise: from chaotic cloud spending to unit economics
An overview of implementing a FinOps culture, the Inform-Optimize-Operate cycle, and unit economics principles to effectively manage cloud spending in the enterprise sector.
Scaling AI-assisted development in enterprise: from code generation to engineering discipline
By 2027, successful AI adoption in development will depend not on code generation speed, but on integrating AI tools into controlled testing, security, and architectural oversight processes.
Integrating AI/ML into products: From prototype to production
Transitioning from experimental ML models to robust enterprise systems using AWS Well-Architected Framework, Google SRE, and 12-Factor App principles.
DevOps and CI/CD: accelerating enterprise time-to-market safely
Balancing software delivery speed and system reliability using DORA metrics, 12-Factor App principles, SRE error budgets, and platform engineering.