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.

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)

Model FinOps is the discipline of understanding, allocating, forecasting, governing and optimising the complete cost of building and operating AI systems. It extends traditional cloud FinOps because AI spending is not driven only by servers, storage and networking—it also depends on tokens, embeddings, retrieval, agents, training, dedicated capacity, evaluation and governance.

The FinOps Foundation frames FinOps for AI as addressing cost complexity, rapid development cycles, unpredictable consumption and the need to connect allocation and optimisation decisions to business value.

The objective is not simply to minimise AI spending. The objective is the best combination of business value, quality, reliability and security for an acceptable total cost, risk and operational complexity. A cheaper model that produces poor answers or requires extensive human correction may be more expensive overall than a more capable model.

A software architect is not simply a senior developer who draws diagrams. The role sits at the intersection of technology, business, delivery, security, operations and leadership.

AI engineering is one of the most in-demand skills in 2026. One of the best books on the topic is AI Engineering by Chip Huyen—all about building applications with foundation models.

ConsultAI OS is the consulting plane of the playbook: situation-first, stage-aware, and built to turn ambiguous AI problems into evidence-backed decisions.