17 posts tagged with "Security"

APIs connect web applications, mobile apps, cloud services, partners, customers, internal systems and increasingly AI agents. They also expose valuable business capabilities directly: creating payments, changing account details, retrieving customer records, submitting claims, placing orders and triggering operational workflows.

That makes API security much broader than adding authentication to an endpoint.

A secure API must verify:

• Who or what is making the request.
• Whether that identity is allowed to perform the requested action.
• Whether it may access the specific object and properties involved.
• Whether the request is structurally and semantically valid.
• Whether the operation is being abused at scale.
• Whether sensitive information is exposed in the response.
• Whether the service and its dependencies remain secure throughout deployment and operation.

The AWS Learning Roadmap follows a sensible progression: learn cloud fundamentals, identity, networking and compute first; then add storage, databases, containers, serverless and operational services. This Azure roadmap follows the same learning philosophy but expands it into an enterprise-grade programme covering cloud architecture, application delivery, AI engineering, cybersecurity, Responsible AI, regulatory compliance and operating-model design.

Terminology note: Microsoft’s current documentation refers to its unified enterprise AI development platform as Microsoft Foundry. You may still encounter the previous “Azure AI Foundry” name in existing projects, articles and interfaces. Microsoft Foundry brings together models, agents, development tooling and production AI operations as an Azure platform service. (Microsoft Learn)

AI agents are moving beyond experimental chatbots into systems that can search enterprise knowledge, call APIs, analyse data, write code, update business systems and coordinate multi-step workflows.

However, building a reliable AI agent is not simply a matter of connecting a large language model to a few tools. Production-grade agents require backend engineering, model knowledge, prompt design, tool orchestration, memory, security, evaluation, observability and operational governance.

DevSecOps integrates security into every stage of software delivery rather than treating it as a final review before release. This expanded guide turns the visual roadmap into a practical learning and implementation programme that engineers, security professionals and technical leaders can follow—from programming and identity through threat modelling, cloud and container security, incident response, enterprise operations and governance.

The original AI Red Teaming roadmap focuses on foundational security, prompt hacking, model vulnerabilities, system security, testing methodologies, tools and professional development. This expanded roadmap turns those topics into a complete enterprise AI-security programme covering the entire lifecycle—from use-case approval and data collection to deployment, monitoring, incident response and retirement.

The central principle is:

Do not secure only the model. Secure the business decision, data, model, retrieval system, agent, tools, application, cloud platform, users and operating process as one connected system.

These frameworks provide structured approaches, tools and methodologies that streamline the development, deployment and management of AI systems. No single library or methodology covers the full journey from ambiguous business problem to governed production capability. End-to-end AI solution engineering therefore uses a stack: consulting and strategy frameworks to decide what to build; data, ML, MLOps and orchestration frameworks to build and run it; domain toolkits for specialised capabilities; and security–governance frameworks as gatekeepers that decide whether work may proceed.

The AWS Learning Roadmap progresses from cloud fundamentals into identity, networking, compute, storage, databases, containers and serverless services. This Google Cloud roadmap follows the same learning sequence, but extends it into enterprise foundations, data engineering, generative AI, agentic AI, cybersecurity, governance, compliance, FinOps and production operations.

Terminology note: Google Cloud’s product naming is evolving, particularly around generative and agentic AI:

• Cloud Functions capabilities are now presented as Cloud Run functions.
• Dataplex Universal Catalog is now Knowledge Catalog.
• Current Vertex AI Agent Engine documentation redirects to the Gemini Enterprise Agent Platform, which includes Agent Runtime, Sessions, Memory Bank, Sandbox, Agent Gateway, evaluation and governance capabilities.
• Vertex AI continues to provide broader machine-learning capabilities such as model development, training, tuning, model management and deployment. (Google Cloud Documentation)