33 posts tagged with "Roadmaps"

Artificial intelligence systems introduce a new class of security problems. A conventional application may fail because of insecure code, weak authentication or an exposed API. An AI application can suffer from all of those problems plus prompt injection, poisoned retrieval data, unsafe tool execution, model extraction, sensitive-data leakage, misleading outputs and autonomous agent behaviour.

AI red teaming is the structured practice of testing these systems from an adversarial perspective. The objective is not simply to make a model produce an inappropriate answer. It is to discover how an attacker, careless user, compromised data source or unexpected interaction could cause the complete AI system to violate its security, safety, privacy or business requirements.

This playbook turns strategy, consulting, architecture, governance, security, delivery, commercial and change frameworks into one practical sequence for taking an AI opportunity from an ambiguous business problem to a scaled, continuously governed production capability.

It is written for AI Solution Engineers, AI Architects, AI Product Managers, enterprise consultants, engineering managers, data and AI leaders, security teams, risk teams and transformation leaders.

The central principle is simple:

Do not begin with a model. Begin with a business outcome, understand the operating context, select the smallest safe intervention that can create measurable value, and build the organisational capability required to sustain it.

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 roadmap in the classic one-page diagram provides a strong high-level sequence: begin with cloud fundamentals, IAM, VPC and EC2; continue into S3, SES, Route 53, CloudWatch and CloudFront; add databases and containers; and finish with serverless computing. The diagram also recommends learning by deploying a simple application rather than studying every AWS service independently.

This expanded roadmap turns that sequence into a practical learning system. It explains what each service does, why and when you would use it, which concepts matter, which exercises to complete, how services work together, which production topics are often missing from one-page maps, and how to progress from a simple application to an enterprise-ready AWS platform.

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.

Becoming an engineering manager is not simply the next promotion after becoming a senior engineer. It is a change in how value is created.

As an individual contributor, you create value primarily through your own technical decisions and implementation. As an engineering manager, you create value by building a system in which people, technology, delivery processes and business priorities work together effectively.

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.

A Forward-Deployed Engineer, commonly called an FDE, works at the intersection of software engineering, product development, consulting and customer delivery.

Unlike a conventional software engineer who may focus mainly on an internal product backlog, an FDE works directly with customers to understand their operations, translate ambiguous requirements into technical solutions, deploy those solutions into real environments and improve the core product based on what is learned in the field.