With the normalization of AI, 4 DevSecOps trends emerge

The role of AI in software development is reaching a crucial moment, one that will force organizations and their leaders to DevSecOps to be more proactive in promoting the effective and responsible use of AI.

At the same time, developers and the broader DevSecOps community must prepare to address at least four global trends in AI: the increased use of AI in code testing, ongoing threats to intellectual property and privacy, an increase in AI bias and, despite all these challenges, increased reliance on AI technologies. Successfully aligning with these trends will position organizations and DevSecOps teams for success. Ignoring them could slow innovation or, worse, derail business strategy.

From luxury to standard: organizations will adopt AI across the board

AI integration will become a standard, not a luxury, across product and service industries, leveraging DevSecOps to create AI functionality along with the software that will use it. Taking advantage of AI to drive innovation and offer greater customer value will be essential to remain competitive in the market driven by this technology.

Based on my conversations with GitLab customers and tracking industry trends, with organizations pushing the boundaries of efficiency through AI adoption, more than two-thirds of companies will incorporate AI capabilities into their offerings by the end of 2024. Organizations are evolving from experimenting with AI to focusing on AI.

Leveraging AI to drive innovation and deliver greater customer value will be critical to remaining competitive in the AI-driven market.

To prepare, organizations should invest in reviewing the governance of software development and emphasize continuous learning and adaptation in AI technologies. This will require a cultural and strategic change. It requires rethinking business processes, product development and customer engagement strategies. And it requires training, which DevSecOps teams say they want and need. In the last DevSecOps Global Report 81% of respondents said they would like more training on how to use AI effectively.

As AI becomes more sophisticated and integral to business operations, companies will need to address the ethical implications and social impacts of their AI-powered solutions, ensuring they contribute positively to their customers and communities.

AI will dominate code testing workflows

The evolution of AI in DevSecOps is already transforming code testing and the trend is expected to accelerate. GitLab research found that only 41% of DevSecOps teams are currently using AI for automated test generation as part of software development, but that number is expected to reach 80% by the end of 2024 and approach 100%. % within two years.

As organizations integrate AI tools into their workflows, they face the challenge of aligning their current processes with the efficiency and scalability benefits that AI can provide. This change promises a radical increase in productivity and accuracy, but also requires significant adjustments to traditional testing functions and practices. Adapting to AI-driven workflows requires training DevSecOps teams in AI monitoring and fine-tuning AI systems to facilitate their integration into code testing to improve the overall quality and reliability of software products.

Additionally, this trend will redefine the role of QA professionals, requiring them to evolve their skills to monitor and improve AI-based testing systems. It is impossible to overstate the importance of human oversight, as AI systems will require continuous monitoring and guidance to be highly effective.

AI threat to intellectual property and privacy in software security will accelerate

The growing adoption of AI-driven code creation increases the risk of AI-introduced vulnerabilities and the potential for widespread intellectual property leaks and data privacy breaches that impact software security, corporate confidentiality, and protection of customer data.

To mitigate those risks, companies must prioritize strong intellectual property and privacy protections in their AI adoption strategies and ensure that AI is deployed with full transparency about how it is used. Implementing strict data governance policies and employing advanced detection systems will be crucial to identifying and addressing AI-related risks. Fostering greater awareness of these issues through employee training and fostering a proactive risk management culture is vital to safeguarding intellectual property and data privacy.

AI security challenges also underscore the constant need to implement DevSecOps practices throughout the software development lifecycle, where security and privacy are not afterthoughts but integral parts of the development process from the beginning. In short, companies must keep security at the forefront when adopting AI to ensure that innovations that leverage AI do not come at the expense of security and privacy.

Prepare for an increase in AI bias even over a period of time

While 2023 was the breakthrough year for AI, its rise highlighted bias in algorithms. AI tools that rely on Internet data for training inherit the full range of biases expressed in online content. This development poses a double challenge: exacerbating existing biases and the emergence of new ones that impact the fairness and impartiality of AI in DevSecOps.

To counteract widespread bias, developers should focus on diversifying their training data sets, incorporating fairness metrics, and implementing bias detection tools into AI models, as well as exploring AI models designed for specific use cases. One promising avenue to explore is to use AI feedback to evaluate AI models based on a clear set of principles, or a “constitution,” that establishes firm guidelines for what AI will and will not do. Establishing ethical guidelines and training interventions is crucial to ensuring unbiased AI results.

Organizations must establish robust data governance frameworks to ensure the quality and reliability of data in their AI systems. AI systems are only as good as the data they process, and bad data can lead to inaccurate results and poor decisions.

Developers and the broader tech community should demand and facilitate the development of unbiased AI through constitutional AI or reinforcement learning with human feedback aimed at reducing bias. This requires a concerted effort among AI providers and users to ensure responsible AI development that prioritizes fairness and transparency.

Preparing for the AI ​​revolution in DevSecOps

As organizations accelerate their shift toward AI-centric business models, it's not just about staying competitive, it's about surviving. Business leaders and DevSecOps teams will need to address the anticipated challenges that are amplified by the use of AI, whether they are threats to privacy, trust in what AI produces, or issues of cultural resistance.

Together, these developments represent a new era in software development and security. Navigating these changes requires a comprehensive approach that encompasses the ethical development and use of AI, vigilant security and governance measures, and a commitment to preserving privacy. The actions DevSecOps organizations and teams take now will set the course for the long-term future of AI in DevSecOps, ensuring its ethical, safe and beneficial implementation.