Cloud and AI security

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Cloud computing and Artificial Intelligence (AI) have become the cornerstone of digital transformation, enabling scalable computing, intelligent automation, data-driven decision-making, and innovative services across industries. The convergence of cloud platforms with AI technologies has accelerated the adoption of machine learning, large language models (LLMs), edge intelligence, and AI-as-a-Service (AIaaS). However, this integration also introduces complex cybersecurity challenges, including cloud infrastructure attacks, AI model compromise, data privacy breaches, insecure APIs, identity and access management issues, supply chain vulnerabilities, adversarial AI attacks, and compliance risks. Ensuring the security, resilience, and trustworthiness of cloud-hosted AI systems is therefore essential for protecting critical applications and sensitive data.

The International Conference on Cyber Security in Artificial Intelligence (ICCSAI 2027) invites original research contributions addressing the latest advances in Cloud and AI Security. This track provides an international forum for researchers, industry experts, cybersecurity professionals, cloud architects, and policymakers to present innovative solutions for securing cloud-native AI applications, AI-enabled cloud services, and distributed intelligent systems. Contributions focusing on secure cloud architectures, AI-driven cloud security, privacy-preserving computing, zero-trust frameworks, secure AI deployment, cloud compliance, and next-generation cyber defense mechanisms are highly encouraged.

Subtopics

1. Cloud Infrastructure Security

  • Cloud Security Architecture

  • Multi-Cloud and Hybrid Cloud Security

  • Cloud-Native Security

  • Virtualization Security

  • Container and Kubernetes Security

  • Serverless Security

  • Infrastructure-as-Code (IaC) Security

  • Software-Defined Networking (SDN) Security

2. AI Security in Cloud Environments

  • AI-as-a-Service (AIaaS) Security

  • Secure Deployment of AI Models

  • Cloud-Based Large Language Model (LLM) Security

  • Foundation Model Security in the Cloud

  • Secure AI Model Hosting and Serving

  • AI Model Lifecycle Security

  • Cloud AI Platform Security

3. Identity and Access Management

  • Cloud Identity and Access Management (IAM)

  • Zero Trust Security for Cloud AI

  • Multi-Factor Authentication (MFA)

  • Privileged Access Management (PAM)

  • Identity Federation and Single Sign-On (SSO)

  • AI-Driven Identity Analytics

4. Data Security and Privacy

  • Cloud Data Protection and Encryption

  • Secure Data Storage and Backup

  • Key Management Systems

  • Privacy-Preserving AI

  • Differential Privacy

  • Federated Learning Security

  • Secure Data Sharing

  • Confidential Computing

5. AI-Driven Cloud Cybersecurity

  • AI-Based Threat Detection

  • AI-Powered Intrusion Detection and Prevention Systems

  • AI for Security Information and Event Management (SIEM)

  • AI for Security Operations Centers (SOC)

  • AI-Assisted Incident Response

  • Cyber Threat Intelligence Using AI

6. Secure AI Development and Operations

  • Secure MLOps

  • AI DevSecOps

  • Continuous Security Monitoring for AI Systems

  • Secure AI Model Training

  • Secure Fine-Tuning of AI Models

  • AI Security Testing and Validation

7. AI Model and Application Security

  • Adversarial Machine Learning

  • Prompt Injection and Jailbreak Attacks

  • AI Model Poisoning

  • Model Extraction and Theft

  • Membership Inference Attacks

  • Model Inversion Attacks

  • AI Hallucination Detection

  • Explainable and Trustworthy AI

8. Cloud Application Security

  • Secure APIs for AI Applications

  • Web Application Security in Cloud Environments

  • Microservices Security

  • API Gateway Security

  • Cloud Workload Protection

  • Secure Software Supply Chains

9. Cloud Network Security

  • Cloud Network Segmentation

  • Secure Virtual Private Clouds (VPCs)

  • Network Access Control

  • Distributed Denial-of-Service (DDoS) Protection

  • Secure Service Mesh Architectures

  • AI-Driven Network Security

10. Compliance, Governance, and Risk

  • Cloud Security Governance

  • AI Governance Frameworks

  • Cloud Risk Assessment

  • Regulatory Compliance (GDPR, HIPAA, ISO 27001, NIST)

  • AI Security Standards

  • Cloud Audit and Compliance Automation

11. Emerging Technologies

  • Edge AI Security

  • Fog Computing Security

  • Internet of Things (IoT) Cloud Security

  • Blockchain for Cloud Security

  • Quantum-Safe Cloud Security

  • Digital Twin Security

  • Autonomous Cloud Security

12. Cloud Resilience and Business Continuity

  • Cloud Disaster Recovery

  • Business Continuity Planning

  • Cyber Resilience for Cloud Services

  • Ransomware Protection in Cloud Environments

  • AI-Based Resilience Monitoring

  • Secure Backup and Recovery

13. Future Directions in Cloud and AI Security

  • Autonomous Security Operations

  • Secure Multi-Agent AI Systems

  • AI Security Benchmarking

  • Cloud AI Supply Chain Security

  • Responsible and Ethical AI in Cloud Computing

  • Green and Sustainable Secure Cloud Computing

  • Secure Multi-Cloud AI Ecosystems

  • AI-Powered Cloud Threat Hunting

 

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