DevSecAI

Services

AI Labs

What is DevSecAI

About

AI Labs

Understanding DevSecAI

DevSecAI integrates security into your AI development, safeguarding every phase against emerging threats.

Get started

Integrating Security from the Ground Up

DevSecAI is the integration of security, privacy, and compliance into every stage of AI development and deployment.

From data pipelines and training workflows to GenAI interfaces and production deployments, DevSecAI ensures your AI systems are protected, trustworthy, and resilient by design. Whether you're adopting LLMs, building ML platforms, or integrating AI into SaaS products, our mission is to make AI secure by default — and scalable without risk.

Why DevSecAI Matters for Your Business

At DevSecAI, we believe that security should be an integral part of your AI development process. From the initial design phase to model deployment and beyond, We embed security at every stage of your AI lifecycle, from model design and infrastructure to the tools your developers use every day.

How DevSecAI Safeguards Your AI Development

Here at DevSecAI, we define AI into two key areas: business AI and Development AI. Business AI includes AI within SaaS platforms, Gen AI, and RPA. Development AI involves taking data and using pre-trained models or building our own LLMs/models. It’s important to separate the two because the processes to protect them are different, but in general, all processes to secure and protect AI are known as DevSecAI.

Here we introduce the AI Development Life Cycle. The scope of which focuses on Development AI.

AIDLC

Stage 1: Problem Statement and Solution - Understanding the data the business currently holds, what problems can be solved with AI or what new products or improvements to existing products can be made, how to utilise the data and how to present it.

Stage 2: Collect and Build - Following solution design, the data required can then be identified and prepared. The AI models or tooling can then be built and trained against the relevant data.

Stage 3: Validate and Test - After the models or tooling are trained, testing and validation is conducted to ensure it meets the business requirement and in some cases continues to learn.

Stage 4: Deploy and Run - Once tuned, the models or tooling can be deployed to production. This includes deployment within Cloud infrastructure such as Sagemaker, Vertex or Azure ML, within Kubernetes or even SaaS services such as Databricks.

Stage 5: Repeat and Revise - When new products and features are developed the process begins again and re-tuning and reassessing is required where necessary.

Here we introduce the AI Development Life Cycle. The scope of which focuses on Development AI.

AI DEVELOPMENT LIFECYCLE (AIDLC)

Stage 2: Collect and Build - Following solution design, the data required can then be identified and prepared. The AI models or tooling can then be built and trained against the relevant data.

Stage 3: Validate and Test - After the models or tooling are trained, testing and validation is conducted to ensure it meets the business requirement and in some cases continues to learn.

Stage 5: Repeat and Revise - When new products and features are developed the process begins again and re-tuning and reassessing is required where necessary.

The DevSecAI Methodology

DevSecAI Embeds security within the AIDLC to become the Secure AIDLC. This structured approach ensures that AI systems are built, deployed, and maintained with security by design from the outset.

Define AI-specific security requirements, AI attacker user stories and regulatory alignment

Embed DevSecAI engineers early to enforce secure-by-design practices during LLM and ML development.

Verify security controls through real-world testing: data poisoning simulation, bias testing,

inference attack simulations, and more.

Attune to threats as models, data, and tools evolve

Iterate to continuously improve proactive monitoring, alerting, incident response and new workflows.

The DevSecAI Framework (DSAIF)

AI Security isn't just about your models—it's a full ecosystem. Our framework ensures security is embedded at every stage of your AI journey through the AI Development Life Cycle.

Discover - Identify your organisation’s AI usage: from tooling

and model versions to access, configuration, and deployments.

Visibility is the first control.

Survey - By assessing risks, tools and use cases, teams must

be trained to challenge AI behaviour, outputs, and

configurations.

Automate - Implement automated defences against model

poisoning, prompt injection, and unsafe LLM usage - tailored

to your organisation’s tooling.

Improve - Continuously improve security controls and upskill

teams through a security-first AI culture.

Forecast - Staying ahead of the ever-evolving threat landscape

to promote future AI innovation.

Benefits from Embedding

Team synergy

DevSecAI embedding encourages collaboration by breaking down silos between data, development, security, and operations teams. Security expertise is built into AI models, identified within development environments, promoting secure coding practices without slowing workflows.

Context-aware decisions

DevSecAI engineers understand the context of code changes and their potential security implications. They learn from data, past security incidents, and current threat intelligence to provide tailored recommendations specific to the organisation's technology stack.

Scalability

As development teams grow and codebases become more complex, DevSecAI engineers ensure consistent security standards are followed. Organisations can scale their efforts by automating security assessments and providing standardised guidance without increasing security overhead.

Adaptive defense

In response to the emergence of new technologies and shifts in application environments, DevSecAI engineers dynamically adjust security measures through best practices and lab research. This ensures the maintenance of robust security controls without the need for delays.