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.
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.
