☑️ ISO/IEC 22989:2022
Information technology - Artificial intelligence - Artificial intelligence concepts and terminology
☑️ ISO/IEC 23053:2022
Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)
☑️ ISO/IEC 23894:2023
Information technology - Artificial intelligence - Guidance on risk management
☑️ ISO/IEC TR 24028:2020
Information technology - Artificial intelligence - Overview of trustworthiness in artificial intelligence
☑️ ISO/IEC 42001:2023
Information Technology - Artificial Intelligence - Management System (AIMS)
☑️ ISO/IEC AWI 42003
Information technology - Artificial intelligence - Guidance on the implementation of ISO/IEC 42001
☑️ ISO/IEC 42005:2025
Information technology - Artificial intelligence (AI) - AI system impact assessment
☑️ ISO/IEC 42006:2025
Information technology - Artificial intelligence - Requirements for bodies providing audit and certification of artificial intelligence management systems
☑️ ISO/IEC AWI 42007
Information technology - Artificial intelligence - High-level framework and guidance for the development of conformity assessment schemes for AI systems
☑️ NIST AI Risk Management Framework (AI RMF)
(Rev 1.0 - 2023)
☑️ NIST SP 800-53
Security and Privacy Controls for Information Systems and Organizations (Rev 5.1.1 - Nov 7, 2023)
☑️ EU AI Act
The AI Act is a European regulation on artificial intelligence (AI)
(July 2024)
☑️ General Data Protection Regulation (EU GDPR)
Europe’s data privacy and security law includes hundreds of pages’ worth of new requirements for organizations around the world
(May 2018)
☑️ ENISA AI Cybersecurity Guidelines
☑️ High-Level Expert Group on AI (HLEG) Ethics Guidelines
⬇️ Technical Part
☑️ MITRE ATLAS™
ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a globally accessible, living knowledge base of adversary tactics and techniques against Al-enabled systems based on real-world attack observations and realistic demonstrations from Al red teams and security groups.
☑️ OWASP AI Security & Privacy Guide
☑️ OWASP GenAI Security Project
☑️ Google Secure AI Framework (SAIF)
#AI standards and frameworks
Information technology - Artificial intelligence - Artificial intelligence concepts and terminology
☑️ ISO/IEC 23053:2022
Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)
☑️ ISO/IEC 23894:2023
Information technology - Artificial intelligence - Guidance on risk management
☑️ ISO/IEC TR 24028:2020
Information technology - Artificial intelligence - Overview of trustworthiness in artificial intelligence
☑️ ISO/IEC 42001:2023
Information Technology - Artificial Intelligence - Management System (AIMS)
☑️ ISO/IEC AWI 42003
Information technology - Artificial intelligence - Guidance on the implementation of ISO/IEC 42001
☑️ ISO/IEC 42005:2025
Information technology - Artificial intelligence (AI) - AI system impact assessment
☑️ ISO/IEC 42006:2025
Information technology - Artificial intelligence - Requirements for bodies providing audit and certification of artificial intelligence management systems
☑️ ISO/IEC AWI 42007
Information technology - Artificial intelligence - High-level framework and guidance for the development of conformity assessment schemes for AI systems
☑️ NIST AI Risk Management Framework (AI RMF)
(Rev 1.0 - 2023)
☑️ NIST SP 800-53
Security and Privacy Controls for Information Systems and Organizations (Rev 5.1.1 - Nov 7, 2023)
☑️ EU AI Act
The AI Act is a European regulation on artificial intelligence (AI)
(July 2024)
☑️ General Data Protection Regulation (EU GDPR)
Europe’s data privacy and security law includes hundreds of pages’ worth of new requirements for organizations around the world
(May 2018)
☑️ ENISA AI Cybersecurity Guidelines
☑️ High-Level Expert Group on AI (HLEG) Ethics Guidelines
⬇️ Technical Part
☑️ MITRE ATLAS™
ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a globally accessible, living knowledge base of adversary tactics and techniques against Al-enabled systems based on real-world attack observations and realistic demonstrations from Al red teams and security groups.
☑️ OWASP AI Security & Privacy Guide
☑️ OWASP GenAI Security Project
☑️ Google Secure AI Framework (SAIF)
#AI standards and frameworks
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2. AI-Optimized Architectures
#AI and machine learning are no longer just add-ons; they are integral to the #architecture itself. AI-optimized architectures involve embedding intelligent algorithms into the core layers of applications, allowing systems to self-optimize in real-time based on changing conditions. These architectures can dynamically adjust resource allocation, optimize workflows, and enhance user experiences through predictive analytics. AI-driven infrastructure management, where the architecture adapts and evolves autonomously, is expected to be a major trend by 2025.
#AI and machine learning are no longer just add-ons; they are integral to the #architecture itself. AI-optimized architectures involve embedding intelligent algorithms into the core layers of applications, allowing systems to self-optimize in real-time based on changing conditions. These architectures can dynamically adjust resource allocation, optimize workflows, and enhance user experiences through predictive analytics. AI-driven infrastructure management, where the architecture adapts and evolves autonomously, is expected to be a major trend by 2025.
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9. Federated and Collaborative AI Systems
In 2025, federated learning and collaborative #AI architectures are enabling multiple organizations to train AI models collectively while keeping their data private. This trend is essential in industries where data sharing is restricted due to privacy regulations. By using decentralized AI training methods, organizations can collaborate on global AI models without exposing their proprietary data. These architectures are particularly important in healthcare, finance, and government sectors.
In 2025, federated learning and collaborative #AI architectures are enabling multiple organizations to train AI models collectively while keeping their data private. This trend is essential in industries where data sharing is restricted due to privacy regulations. By using decentralized AI training methods, organizations can collaborate on global AI models without exposing their proprietary data. These architectures are particularly important in healthcare, finance, and government sectors.
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From Smart Home To Intelligent Home
What’s the difference? Well, smart home devices have been with us for a while, and often they aren’t really that smart—they’re just connected (think of lightbulbs, heating systems and kitchen appliances we can control over the internet). Truly intelligent homes, however, utilize AI to “think” and make decisions for us. This means AI assistants acting as virtual housekeepers, coordinating the activity of smart appliances, entertainment and security devices.
#ai #smart #intelligent
What’s the difference? Well, smart home devices have been with us for a while, and often they aren’t really that smart—they’re just connected (think of lightbulbs, heating systems and kitchen appliances we can control over the internet). Truly intelligent homes, however, utilize AI to “think” and make decisions for us. This means AI assistants acting as virtual housekeepers, coordinating the activity of smart appliances, entertainment and security devices.
#ai #smart #intelligent
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Among technology trends in 2026, industry-specific #AI trained on specialized data and domain knowledge stands out for creating competitive advantages that general-purpose AI models cannot replicate. General AI models know a little about everything but aren't experts in anything. Vertical AI models are trained specifically for one industry: healthcare, legal, logistics, or manufacturing, ...
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