How to Embed GRC into Continuous Software Delivery
Post Summary
Introduction
In today’s fast-paced digital landscape, where healthcare professionals and cybersecurity leaders are navigating the realms of advanced software delivery and governance, a significant challenge persists - how to effectively integrate Governance, Risk, and Compliance (GRC) practices into continuous software delivery without sacrificing speed or quality. With the advent of generative AI (GenAI) accelerating delivery cycles, the gap between risk management frameworks and software engineering workflows continues to widen.
This article delves into how embedding GRC into software delivery pipelines can bridge this divide, ensuring compliance, security, and operational efficiency. Drawing from insights shared by Magnus (CEO of Nooga) and Emilie (COO of Nooga), experts with backgrounds in software engineering and governance, this discussion highlights practical strategies, challenges, and opportunities for healthcare and cybersecurity professionals to align risk management with the imperatives of continuous delivery.
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The Evolution of Software Delivery and GRC
The Software Engineering Perspective
The origins of software delivery can be traced to the "software crisis" of the 1960s, which led to the emergence of structured methodologies like Agile and, eventually, DevOps by 2007-2008. These practices emphasized automation, tooling, and cross-functional collaboration to scale delivery without compromising quality. Today, continuous delivery enables organizations to deploy new features in real time, sometimes hundreds of times a day.
However, the integration of GenAI into software engineering is creating a new paradigm. AI tools, such as GitHub Copilot, are helping developers automate repetitive tasks like documentation and bug fixes, but they also introduce risks, such as stability concerns and compliance gaps. As Magnus explained, while GenAI accelerates tedious workflows, its unchecked use could lead to systemic vulnerabilities, thus heightening the importance of robust GRC frameworks.
The GRC Perspective
On the flip side, GRC practices have historically evolved in response to crises, such as the Enron scandal in the early 2000s and the 2008 financial crisis. Regulatory frameworks like the EU’s Digital Operational Resilience Act (DORA) emphasize ICT risk management and change management, yet they remain rooted in linear, project-based models rather than the continuous nature of modern software delivery.
As Emilie noted, many risk management processes remain siloed in spreadsheets or disconnected systems, making them ineffective for today’s agile environments. These legacy approaches fail to align with the pace of software delivery, leaving healthcare organizations and cybersecurity teams struggling to comply with ever-evolving regulations while managing risks in real-time workflows.
Key Challenges in Aligning GRC with Software Delivery
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Siloed Risk Management Practices
Risk management tools are often detached from delivery workflows, making it difficult for decision-makers to access real-time data. For instance, GRC systems may produce automated reports, but if they are not integrated into the daily tools used by development teams, they fail to influence decisions where they matter most. -
Outdated Assessment Cycles
Many organizations still conduct risk assessments quarterly or sporadically, a stark contrast to the continuous nature of software delivery. This misalignment leads to reactive, rather than proactive, risk mitigation strategies. -
Contextless Risk Assessments
Risk assessments often lack alignment with the specific context of delivery tasks. Emilie recounted how one organization sent a 100-question survey to development teams, many of whose answers were already available in their systems. This inefficiency underscores the need for smarter risk management tools that derive insights directly from operational platforms. -
Scaling Expertise Amid Resource Constraints
Risk management experts are often outnumbered, with one GRC professional supporting hundreds of developers. This disparity creates bottlenecks, as complex governance structures can delay product launches. Emilie highlighted a case where a new banking product was shelved for over a year due to governance inefficiencies. -
AI-Driven Risks
While AI tools enhance productivity, they also pose risks to system stability. Surveys have shown that developers fear a 25% increase in AI use could compromise the robustness of the software they deliver. Additionally, developers are seeking clearer policies to guide responsible AI usage and mitigate unintended risks.
Bridging the Gap: Embedding GRC into Software Delivery
The DevOps Lesson
The DevOps movement offers critical lessons for integrating GRC into continuous delivery. By uniting once-siloed teams (development and operations) under shared goals and integrating automation and tooling, DevOps achieved faster delivery cycles without compromising stability. Similarly, embedding GRC into software teams’ existing workflows can enable them to manage risks effectively without slowing delivery.
Embedding Risk Management in Daily Workflows
The key to aligning GRC with software delivery lies in integration. Risk management must transition from isolated systems to being embedded directly into the platforms where teams work daily. For example, incorporating GRC tools into platforms like Azure DevOps allows developers to assess and manage risks in real-time as they plan, code, and deploy.
As Magnus explained, the goal is for risk management to feel intuitive and natural rather than an added burden. AI can play a vital role by analyzing workflows and generating contextual risk insights, scaling the impact of GRC experts across large organizations.
Leveraging AI to Scale GRC Expertise
AI-powered tools can bridge the gap between GRC professionals and delivery teams by automating repetitive tasks, such as risk identification and compliance checks. These tools can analyze data from delivery platforms and provide actionable insights, enabling GRC experts to focus on high-priority risks. For example:
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Contextual Risk Insights
AI can identify potential risks within specific delivery tasks, allowing teams to assess and address them before they escalate. -
Proactive Risk Monitoring
By continuously monitoring workflows, AI ensures that risk assessments are aligned with delivery cycles, reducing the reliance on periodic reviews. -
Enhanced Collaboration
Integrated platforms facilitate collaboration between GRC experts and developers, ensuring that both sides share context and contribute to informed decision-making.
The Future of GRC in Software Delivery
The convergence of GRC and continuous delivery is not just an operational necessity - it’s a strategic imperative for healthcare and cybersecurity organizations. In an era defined by AI, cyber threats, and regulatory complexities, embedding GRC into delivery pipelines ensures that organizations remain agile, compliant, and resilient.
As Emilie emphasized, the ultimate goal is to enable secure and compliant delivery without slowing down innovation. By placing risk management where decisions are made and leveraging AI to scale expertise, organizations can achieve a balance between speed and control that safeguards both their operations and their patients.
Key Takeaways
- GRC and software delivery must evolve together: Traditional risk management systems are incompatible with the pace of continuous delivery. Integration is key.
- Embed GRC into daily workflows: Risk management tools should operate within the platforms where delivery teams work, such as Azure DevOps, ensuring real-time alignment.
- Leverage AI for scalability: AI-powered tools can automate risk identification and provide contextual insights, mitigating the resource gap between GRC experts and developers.
- Proactive risk management is essential: Quarterly assessments are insufficient in the era of continuous delivery. Organizations must adopt real-time monitoring and adjustment strategies.
- Regulations must adapt to reflect continuous delivery: Frameworks like DORA need to consider the agile nature of modern software development, moving away from start-and-stop models.
- Collaboration is critical: Bridging the gap between GRC and engineering requires shared tools, context, and goals, much like the DevOps movement achieved between development and operations.
Conclusion
The healthcare and cybersecurity sectors cannot afford to view GRC and software delivery as separate domains. In a world where digital innovation drives both opportunity and risk, integrating GRC into software delivery pipelines is no longer optional - it’s essential for maintaining compliance, security, and operational excellence.
By learning from the lessons of DevOps, leveraging AI, and embedding risk management into the tools and workflows of delivery teams, organizations can ensure that speed and control coexist seamlessly. The future of GRC lies not in standalone systems but in contextual, integrated solutions that empower teams to make informed, secure decisions at the pace of modern innovation.
Source: "When AI Ships Fast, Can GRC Keep Up?" - Nooga, YouTube, Nov 18, 2025 - https://www.youtube.com/watch?v=O4cHm424g8s
