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Developing an effective AI governance framework is essential for any company that intends to or has started to deploy AI-dependent tools anywhere within its operations, including within the software development function. However, a disconnect often arises between the DevOps teams building AI-driven technologies into existing or planned applications and the security leaders implementing large language models (LLMs) and other generative AI (GenAI) tools across the broader company population. This blog post explores the critical miscommunications that occur and offers strategies to bridge the gap, ensuring robust AI governance and security.

The Disconnect Between DevOps and DigitalSec Teams

The DevOps teams and the digital security teams often have different priorities and perspectives. DevOps professionals are typically focused on the technical aspects of AI implementation, striving for efficiency, innovation, and rapid deployment. They are the ones grappling with the practical challenges of integrating AI tools into existing systems, addressing technical debt, and managing continuous delivery pipelines.

Conversely, security professionals and practitioners prioritize strategic oversight, risk management, and regulatory compliance. Their concerns center around the broader implications of AI adoption, such as the potential for data breaches, compliance with regulations like the General Data Protection Regulation (GDPR), and the impact on the company’s reputation. This divergence in focus can lead to communication breakdowns, where each side talks past the other, hindering effective AI governance.

Common Miscommunications

Risk Perception

DevOps teams often view AI tools as enablers of innovation and productivity. They may perceive the risks associated with these technologies as manageable within their technical frameworks. On the other hand, Security sees AI risks from a broader perspective, encompassing financial, legal, and reputational damage. This difference in risk perception can lead to underestimation or overemphasis of certain risks, creating friction in governance discussions.

Pace of Implementation

DevOps teams thrive on agility and speed, aiming to deploy AI solutions quickly to gain competitive advantages. Security, however, may advocate for a more measured approach, emphasizing the need for comprehensive risk assessments and robust governance frameworks before deployment. This clash can result in delays or, conversely, rushed implementations without proper oversight.

Technical Complexity vs. Strategic Oversight

DevOps professionals dig into the technical complexities of AI, focusing on model performance, data pipelines, and integration challenges. Security teams focus on strategic oversight, governance principles, and regulatory compliance. This disparity can lead to misaligned expectations and a lack of shared understanding regarding the necessary governance measures.

Bridging the Gap

To ensure effective AI governance, it is critical to bridge the communication gap between DevOps and Security. Several key strategies can facilitate better dialogue and collaboration, including:

Establish Clear Communication Channels

Regular, structured communication channels should be established between DevOps and Security, including establishing cross-functional teams with scheduled meetings and designated liaisons who can translate technical details into strategic insights–and vice versa–that can be shared with the broader organizational population.

Align on Governance Principles

Both sides must agree on foundational AI governance principles that align with the organization’s values and regulatory requirements. These principles must address key concerns such as data privacy, ethical AI use, and risk management. Examples include:

  • All LLM output will be reviewed and owned by a person.
  • LLM-generated software code must be checked by a human before integration.
  • Internal communications involving LLMs should be documented and transparent.

Risk Assessment and Mitigation

Develop a shared risk assessment framework that combines technical and strategic perspectives. DevOps can provide insights into technical vulnerabilities and mitigation strategies, while Security can assess the broader implications of these risks. This collaborative approach ensures a comprehensive understanding of potential threats and appropriate safeguards.

Joint Training and Education

Conduct joint training sessions and workshops to enhance mutual understanding of AI technologies and governance needs. DevOps teams should gain insights into regulatory landscapes and strategic considerations, while the Security teams should familiarize themselves with the technical intricacies and limitations of AI tools.

Leverage Cross-Functional Teams

Form cross-functional teams that include representatives from Operations, HR, Legal, Compliance, and other relevant functions. These teams can collaboratively develop and implement AI governance frameworks, ensuring that all perspectives are considered and integrated.

Case Study: AI Governance in Practice

Consider a financial services company integrating AI into its customer service operations. The DevOps team sees the potential for AI to streamline processes and improve customer experiences, and pushes for rapid deployment. Security, however, is concerned about the risk of data breaches, as well as ensuring the deployment does not put the company out of compliance with financial regulations.

Having a cross-functional AI governance team review, assess, and decide on the best course of action leads to the development and implementation of: 

  • Regular risk assessments and audits to ensure compliance.
  • Clear guidelines for AI use and data handling.
  • Ongoing training for both teams, as well as other teams or individuals involved.

This collaborative approach bridges the communication gap and ensures that AI adoption aligns with the company’s strategic goals and regulatory requirements.

The integration of AI and ML technologies in enterprises necessitates robust governance frameworks to manage expanding risks and ensure compliance. Bridging the communication gap between DevOps and Security is crucial to achieving this goal. Clear communication channels, alignment on governance principles, and cross-functional collaboration can enhance a company’s AI governance efforts and ultimately lead to more secure and compliant AI deployments. As AI continues to evolve, nurturing a culture of shared understanding and mutual respect between key internal teams will be key to navigating the complexities of AI governance.

Read more about developing a comprehensive AI governance framework in our Strategic Blueprint for AI Adoption, available here


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