AI & Emerging Technologies

Cloud computing security blind spots created by AI stem from the complexity of cloud infrastructure and unique challenges AI introduces in this environment. These blind spots can reveal vulnerabilities that traditional security measures such as strong governance, transparent AI model development, and robust data management practice may not fully address. Here, I offer some suggestions […]

Faster & Error-Free Deployments – AI can detect and prevent misconfigurations in IaC scripts before deployment – It enables automated remediation, reduces human intervention which results in minimizing downtime.Intelligent Automation & Optimization – AI can analyze historical deployment data to recommend most efficient configs – It automates Performance tuning, ensures optimal resource allocation which contributes

Fairness and Biased Mitigation – Devs should work on techniques to detect and minimize bias, such as: re-sampling data, adversarial debiasing, & any fairness constraints in model training. AI systems should be inclusive and not propagate biases present in training data or Devs assumptionsExplainability & Transparency – Algorithm should be understandable to Developers (Devs), stakeholders,

Improved Productivity – It automates repetitive tasks e.g. report writing, email drafting, & documentation, saves time & effortContent Generation with Enhanced Creativity – GenAI can generate text, images, code, and music, enables businesses & creative professionals to automate content creation.Automates Software Development – Assists in code generation, bug fixing, and documentation, accelerates software development cyclesAdvanced

High CPU/GPU Requirements – Be aware that your VM has sufficient resources i.e. to avoid bottlenecks. CPU, GPU, and memory, to perform resource-intensive functions – AI workloads can demand dynamic scaling of resources, especially for cloud-based VMsNetwork Latency – If large datasets stored in multiple locations, network latency can become a significant performance bottleneck.Cost Management

Proactive Threat Detection – AI identifies potential threats before they can cause any damage – Technically, AI doesn’t sleep – it monitors systems 24/7, identifying even smallest anomalies. It analyzes vast amounts of data in seconds, reducing time to detect & mitigate risksPersonalized Defense Mechanisms – AI adapts to specific needs of an Org., provides

Automation – AI has significantly automated routine & repetitive coding tasks, allowing Dev to focus on complex & creative aspects of software development. For example, AI-powered tools, i.e. GitHub Copilot & Tabnine offer code suggestions & help with boilerplate code generation.Debugging & Code Review – AI transforms the way code is reviewed & debugged with

Anomaly Detection – AI can analyze large volumes of VPN traffic & identify patterns of normal activity. When deviations from these patterns occur, i.e. unusual login times, locations, or data transfer volumes, AI systems can flag and or block potentially malicious activities in real time. Machine learning models can predict threats based on historical dataSelf-Healing