AI in Multi-Cloud Risk Mitigation
AI monitors, detects and automates security, compliance and FinOps across multiple cloud providers to reduce risks and cut costs.
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Blog posts in the Automation category
Behavioural Anomaly Detection: Use Cases in DevOps
Behavioural anomaly detection uses ML on logs, metrics and traces to detect infrastructure, performance and security issues in cloud-native DevOps.
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How AI Improves CI/CD Tool Security
AI secures CI/CD pipelines with real-time code and log analysis, automated vulnerability scanning and remediation, fewer false positives and automated compliance.
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Ultimate Guide to CI/CD Compliance Scanning
Embed automated compliance scans into CI/CD to find issues early—shift-left checks, policy-as-code, SBOMs and layered SAST/DAST/SCA for UK regulatory readiness.
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Best Practices for CI/CD Feedback Loops
Actionable CI/CD guidance on automating tests, monitoring pipelines, shifting security left and improving collaboration to speed delivery and reduce failures.
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How to Track Idle Cloud Resources
Practical steps to find and manage idle cloud resources — tagging, monitoring, automation and audits to reduce waste and lower cloud costs.
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AWS IAM Audit: Evidence Management Tips
Automate IAM evidence collection with Audit Manager, CloudTrail and AWS Config; organise S3 storage, enable full logging and stay audit-ready.
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Designing GDPR-Compliant IAM Policies: A Guide
Step-by-step guidance to build GDPR-compliant IAM: RBAC/ABAC choices, MFA, automated provisioning, audits and data minimisation to reduce risk.
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Cross-Platform Patch Management: Best Practices
Unify patching for Windows, macOS, Linux and third-party apps: maintain an asset inventory, prioritise by CVSS, and automate testing and deployment for security and compliance.
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IAM Policies for HIPAA: Checklist for Compliance
Practical IAM checklist to meet HIPAA Privacy, Security and Breach Notification rules — MFA, RBAC, encryption, audit logging, BAAs and six-year records.
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How to Design Energy-Efficient Hybrid Cloud Systems
Practical steps to cut energy use in hybrid cloud: audit resources, right-size workloads, use efficient hardware and cooling, and automate monitoring and scaling.
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AI in Kubernetes Monitoring: Risks and Benefits
AI shifts Kubernetes monitoring from reactive noise to proactive, predictive observability—improving detection and efficiency but requiring extra compute and specialist skills.
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