Build and manage AI systems that are reliable, secure, and compliant.
Unlike conventional software, which follows fixed, pre-programmed rules, AI learns, evolves, and makes autonomous decisions. To manage AI-specific risks, AI systems need to adhere to strong engineering practices.
We see many organizations struggling to transition AI from experimental projects in the lab to scalable, secure, compliant, and maintainable real-world applications. AI also needs regular retraining to stay accurate.
The engineering challenges stem from how AI engineers—such as data scientists—are traditionally managed and trained.
Their focus is often on quickly creating insights and models, not on building systems that are secure, reliable, maintainable, reusable, easy to transfer, or testable.
AI systems often have long, unfocused code blocks handling multiple responsibilities, making them difficult to modify, analyze, and reuse. In addition, due to a lack of testability, errors are harder to detect and AI models are riskier to update.Â
Over time, as data and requirements change, adjustments are typically ‘patched on’ rather than properly integrated, making things even more complicated. Furthermore, transferring to another team becomes less feasible.
In AI systems, teams are often composed mainly of data scientists, whose focus on creating functional models can lead to a lack of software engineering best practices, ultimately causing quality and security issues.
Also, traditional data science development tools offer little support for software engineering best practices. Tools are designed for experiments, not creating maintainable software, and some data science languages lack powerful abstraction and testing mechanisms.
Once the model works, there’s little incentive to improve the code.
Know exactly which AI technologies are in use
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Set quality goals that adapt to your AI maturity
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Design AI systems that won’t become liabilities
Leverage 25+ years of expertise to ensure that the systems you develop and manage are reliable, secure, and scalable.
Bring discipline and consistency to the AI lifecycle
Align with the global standard we helped create, the ISO/IEC 5338. Ensure your AI can go beyond experimentation and in production.
Resolve issues and risks specific to AI
Identify AI across your portfolio
Apply tech-readiness evaluations.
Achieve desired levels of quality
Align your AI development with best practices
Evaluate how your teams build, deploy, and maintain AI systems
Get a deep analysis of your AI systems.
Related resources
Unlike conventional software, which follows fixed, pre-programmed rules, AI learns, evolves, and makes autonomous decisions.Â
According to ISO/IEC 5338, co-developed by Software Improvement Group (SIG), AI is classified as a software system with unique characteristics. These include the ability to think autonomously, learn from data, make decisions based on that data, and even potentially talk, see, listen, and move.Â
In addition, AI needs regular retraining.Â
What sets AI software apart from traditional software is that it doesn’t just follow fixed rules. Instead, it learns by analyzing large sets of data, finding patterns, and using that knowledge to make educated guesses when making decisions, solving problems, or answering questions.Â
The AI Practices Assessment (AIPA) benchmarks your AI lifecycle against ISO/IEC 5338, showing whether your development processes are reliable, compliant, and aligned with engineering best practices.
The Development Practices Assessment (DPA) identifies strengths and gaps across people, process, and technology. It highlights where AI development workflows need improvement to support consistency, quality, and scalability.
A Software Risk Assessment (SRA) evaluates system architecture, maintainability, and operational risks. It identifies modernization needs that improve performance, resilience, and long-term sustainability.
Sigrid® provides continuous insight across codebases built on 300+ technologies and 30,000+ systems. It automatically detects AI-related risks and delivers recommendations aligned with ISO standards.
Yes — AI system detection identifies where AI appears across your portfolio, the dependencies involved, and risks tied to AI components. This supports better governance, risk management, and reliable scaling.
If you have questions that aren’t covered here, feel free to reach out. We’re always happy to help you with more information or clarifications.