Artificial intelligence is rewriting the rules of software development in real-time. Developers can leverage AI to generate code, flag vulnerabilities, and automate the testing process (checking whether new code works as intended and meets security standards). From enterprises to small businesses, AI is streamlining work that once took hours or days and allowing developers, IT professionals, and others to focus on mission-specific tasks.
The ROI businesses can reap from AI is real and can be, in many cases, game changing. However, this fundamental change to the way we work also introduces a challenge that many organizations are overlooking that the old ways of ensuring security, accountability, and quality no longer fit the pace and complexity of AI-driven workflows.
AI is becoming increasingly ingrained in our workflows, increasing the risk of introducing vulnerabilities, misconfigurations, or compliance gaps, often without a clear audit trail. The speed that makes using automation appealing can also make errors harder to catch, leaving organizations exposed to risks that traditional oversight processes weren’t designed to handle.
As AI moves deeper into production, rather than streamlining operational tasks, back-end and internal environments, roles, and responsibilities within IT and development teams must evolve. Security reviews can no longer be siloed with one person or department at the end of the process, and safeguards need to be built in from the start and shared across developer, security, and operations teams. Here’s what effective adoption looks like:
Rethinking your AI guardrails
According to Microsoft’s 2024 Data Security Index, incidents linked to AI applications surged from 27% in 2023 to 40% in 2024, highlighting a significant rise in security threats as AI tools become more common in enterprise environments. When integrating AI into your workflows, your guardrails or safety measures can’t just be static checklists.
In tax technology, for example, an AI model might automatically update compliance rules to reflect rate changes across multiple jurisdictions. If that update isn’t validated and logged properly, it could lead to incorrect filings or missed deadlines, triggering audits, fines, delayed payments, or costly penalties.
Organizations should implement human-in-the-loop review, at a minimum, so that AI-generated changes are checked by qualified staff before going live. Other safety guardrails include role-based access controls to ensure only authorized users can approve updates and continuous monitoring to catch errors or anomalies in real-time. Taking these steps turns AI from a potential liability into a reliable partner, while keeping compliance and security at the forefront.
Tracking provenance and clear accountability
As we rely on AI to generate updates, to code, for example, the “who wrote this?” becomes less clear, which can be dangerous in a security incident. Without clear provenance tracking, organizations risk using outdated or incorrect data that could lead to compliance violations.
Imagine an AI system that is responsible for updating multiple tax rules across several jurisdictions: Without a clear record and method to track these changes, it’s hard to tell which rule is currently active, who approved the updates, or whether the data is correct. Without this clarity, errors can slip through, potentially leading to costly fines or compliance issues.
Embedding provenance tracking directly into workflows, such as CI/CD pipelines, ensures every AI-generated line can be traced back to a known, vetted source. This not only protects security and compliance but also builds a record of accountability that becomes critical during audits or incident investigations.
Equally important is assigning “code owners” for every AI-generated component so a specific person is always responsible for review and approval. This approach will dramatically reduce the time it takes to identify and resolve issues and give both leadership and regulators greater confidence in your security posture and governance processes.
Preparing your team for the new normal
AI is reshaping the way teams work and the skills required for many jobs. It’s no longer enough for individuals to focus solely on their own tasks. Developers, compliance officers, and security teams must collaborate closely to review AI-generated outputs, identify potential issues, and know when to escalate concerns.
Yet, despite 74% of employees using AI at work, only 33% have received formal training on its use. This gap in AI literacy highlights the urgent need for structured programs that build a culture of shared responsibility and continuous learning at businesses. Otherwise, organizations risk misusing AI and potentially missing critical, costly errors.
The long-term impact of artificial intelligence will not just affect tools. It will fundamentally reshape roles, expectations, and team structures. This shift demands new skills and training that help teams understand both the capabilities and the limitations of AI tools. It also requires that organizations create a culture of shared responsibility, accountability, and continuous learning.
The question isn’t whether teams can move faster: It’s whether they can move faster, safely. Organizations that adapt now will be better equipped to innovate without sacrificing security or trust as AI becomes more deeply embedded in the development lifecycle. AI is a core part of software development and compliance operations, and it will continue to evolve, and so must the guardrails that govern it. Those that adapt now and implement the right guardrails and team structures will be best positioned to innovate without sacrificing security or trust.
