The use of certain DevSecOps tools

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Third, conservatism. Implementing DevSecOps requires major changes to the development process itself, and many companies are wary of the risk of failures and additional costs at the initial stages of the transition. This leads to resistance from employees and management, who may see the changes as a threat to stability and efficiency.

for security automation, testing, and monitoring also poses challenges. Not all popular tools integrate with existing systems, especially when it comes to older or proprietary solutions, which complicates the transition to a new methodology. In addition, there are a number of legislative acts in Russia that may limit the ability to integrate certain security-related solutions and processes. For example, requirements for the protection of personal data and other regulations may create additional difficulties when implementing DevSecOps.

In fact, DevSecOps is a powerful approach that allows organizations to integrate security into all stages of software development, making it more secure and high-quality. Its implementation in Russia today is indeed facing a number of challenges, ranging from a shortage of experts to resistance to change within companies. To overcome these obstacles, it is necessary to develop a security culture, invest in personnel training, and look for solutions that will allow DevSecOps to be smoothly integrated into existing processes. This is the only way Russian companies will be able to effectively protect their digital products and meet global software development standards.
Lean AI: Small Language Models Are Game-Changing
11.09.2024
Artificial intelligence is undergoing significant benin mobile database in the corporate landscape as businesses seek more efficient and cost-effective approaches, writes David Linthicum, chief cloud strategist at Deloitte Consulting, in eWEEK .

Lean AI—the practice of developing small language models (SLMs) that are more efficient and less computationally intensive than traditional large language models (LLMs)—has emerged as a strategy that prioritizes minimal resource consumption while delivering maximum business value. It uses approaches borrowed from lean manufacturing, a methodology originally used in product development and manufacturing, to optimize the creation and deployment of AI systems.

Lean AI is becoming increasingly relevant as companies look for more efficient ways to optimize their cloud computing spend. This approach prioritizes flexible, data-driven decision making and continuous improvement, allowing businesses to harness the power of AI in a sustainable and scalable way. Here’s what you need to know.