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Keeping Your AI Governance in Pace with AI Innovation: Enabling Your Organization without Taking Risk Breadcrumb Home Insights Blog Keeping Your AI Governance in Pace with AI Innovation: Enabling Your Organization without Taking Risk February 28, 2025 Critical AI governance needs are easily overlooked in the rush to adopt artificial intelligence (AI) to tap into all the opportunities it offers. Unchecked, AI introduces significant risks. However, when AI is paired with responsible governance, organizations can foster innovation while supporting ethical practices, operational efficiency and compliance goals. Optiv helps organizations bridge the gap between innovation and governance. Sustainable, repeatable and responsible governance frameworks are essential to realizing AI's full potential. Challenges of AI Governance Adopting AI at scale is no small feat. Many organizations face substantial challenges while maintaining effective governance and trying to keep pace with AI innovation. These obstacles often stem from insufficient maturity in governance practices, limited understanding of AI risks and a lack of alignment across teams. Layers of AI Governance AI governance overlaps with and must integrate across other governance capabilities, including data governance, identity governance, privacy and ethical governance as well as overarching risk governance. AI Outpaces Governance Capabilities Some organizations adopt AI faster than they can establish governance frameworks to support it. Others resist AI adoption but have few protocols in place to manage their AI risks and protect from shadow AI (the unsanctioned use of AI tools or applications). These gaps result in varying levels of readiness. Both scenarios leave organizations vulnerable to compliance failures, security gaps and operational inefficiencies. Knowledge Gaps In many organizations, IT and security teams lack the expertise needed to identify and mitigate AI-specific risks. Meanwhile, business units often underestimate the potential consequences of unchecked AI adoption. This misalignment between teams makes establishing effective governance difficult. Risks of Inadequate Governance Without proper governance, AI adoption introduces substantial risks that can disrupt operations, expose vulnerabilities and harm reputations. Data Privacy and Protection: AI systems often require access to sensitive data for training and operations. Without governance in place, organizations risk mishandling this data, leading to breaches or non-compliance with regulations like GDPR or CCPA Data Quality Issues: The quality of AI outputs is only as good as the data on which they are trained. Poor or biased data can lead to flawed insights and decisions, eroding the reliability of AI systems Over-Reliance on AI: Many organizations place blind trust in AI systems, overlooking potential errors, bias, drift or hallucination. This over-reliance can lead to operational failures when AI systems encounter unforeseen scenarios or fringe prompts Content Anomalies: AI models can produce inappropriate, biased or otherwise harmful content if not carefully monitored. These content anomalies can damage brand reputation and customer trust Unpredictability: Unlike traditional IT systems, AI has non-deterministic outputs, meaning it can make decisions that are not always predictable. This unpredictability requires human oversight, especially in the early stages of deployment, to validate outcomes Ethical Concerns: Without responsible and ethical AI practices, organizations risk deploying systems that reinforce biases, make unfair decisions or fail to meet stakeholder expectations for transparency and accountability Image How to Build Robust and Scalable AI Governance Frameworks Organizations adopting AI to drive innovation face significant risks tied to data security, compliance and operational integrity. To navigate these challenges, businesses can implement governance frameworks designed to address both immediate risks and the long-term implications of AI adoption. These frameworks should empower organizations to balance innovation with responsibility, enabling secure, scalable and ethical AI integration. Critical Components of a Security-First Framework The foundation of a robust AI governance framework lies in its ability to prioritize security at every level. Security-first frameworks go beyond mitigating vulnerabilities — they set the standard for compliance, promoting ethical AI practices and aligning technology with business goals. Below are the essential components for creating an effective governance structure: Risk Identification and Mitigation Effective AI governance begins with identifying and addressing risks associated with AI systems. Organizations should: Develop risk registries: Include ethics, explainability and responsibility alongside traditional cybersecurity concerns to provide a comprehensive view of potential vulnerabilities Implement human-in-the-loop (HITL) models: Validate AI decisions during deployment to align with ethical and organizational objectives Lightweight and Scalable Processes Governance frameworks should be adaptable to meet the needs of evolving AI technologies. Leaders should: Introduce self-service or automated impact assessments: Streamline workflows to reduce bottlenecks and enable faster decision-making Educate teams on AI risks: Foster accountability and transparency across departments by providing targeted training and resources Adopt Industry Standards Industry frameworks provide a strong foundation for building structured and scalable governance practices and there are several to use. NIST AI RMF: A framework for managing AI risks and ensuring trustworthy AI systems OWASP AI Exchange: Security best practices and tools for AI and machine learning Mitre ATLAS: A threat framework mapping AI attack techniques and defenses These and other leading standards can assist in creating practices that are tailored to organizational needs and align with global compliance requirements. Interdepartmental Collaboration and Transparency Strong governance frameworks rely on collaboration across departments and a commitment to transparency. To achieve this, organizations should prioritize: Collaboration Across Teams: When teams work together, AI risks can be addressed holistically, and innovation efforts remain secure Transparency and Education: Use governance processes to educate teams about risks while fostering openness about AI’s capabilities and limitations to help teams identify and address challenges early Image What Can Leaders Do? CISOs and senior leaders play an essential role in implementing AI governance frameworks that effectively balance innovation with risk management. Leadership is not just about setting strategies — it is about driving organizational alignment, investing in education and preparing for the future. As AI evolves, so must the governance frameworks that support it and leaders should be at the forefront of this effort. This dual focus — fostering innovation while mitigating risks — requires deliberate action and strategic leadership. Champion AI Literacy Initiatives Leadership begins with setting the tone for responsible AI adoption. This means prioritizing education and funding initiatives that build AI literacy across the organization. Fund education programs: Provide IT, security and business teams with the training needed to understand AI risks and opportunities Establish governance frameworks: Create scalable structures that align with business goals while managing AI risks Champion cross-departmental collaboration: Give every team what they need to work toward shared objectives in innovation and governance When leaders set these priorities, they enable their organizations to innovate responsibly, leveraging AI for growth while maintaining compliance and accountability. Foster Cross-Functional Alignment AI governance requires collaboration across IT, security and business units. Leaders can bridge these silos to align strategies for innovation and risk mitigation. “All critical parties need to align on objectives, threats, challenges and how to manage them. Misalignment can lead to governance gaps that expose the organization to unnecessary risks. Shared accountability is a critical element of alignment. In a culture where all teams take responsibility for AI risks and benefits, no single department operates in isolation. Encourage Transparency and Education Transparency is essential for fostering trust and alignment across teams. Leaders should use governance processes to educate teams about risks and promote openness about how AI technologies are being adopted. By creating clear communication channels: Teams understand both the capabilities and limitations of AI tools Security and IT departments are empowered to identify potential risks early Business units are better equipped to manage and mitigate the impact of AI on organizational objectives Encouraging open discussions about AI tools and their implications strengthens collaboration and builds a shared sense of responsibility across departments. Adopt Ethical and Compliant AI Governance Ethical and compliant AI governance is foundational to building trust in AI systems. Leaders should prioritize fairness, transparency and accountability to reduce bias and meet regulatory requirements. This involves creating structures that help AI systems operate responsibly and align with organizational values. Make Compliance a Governance Pillar Regulatory frameworks like GDPR and AI-specific standards require leaders to adopt robust compliance structures. Leveraging tools such as NIST AI RMF, NIST Privacy Framework, OWASP AI Exchange and OWASP Top 10 for LLMs can help organizations manage risks and meet evolving regulations Adopt Human-in-the-Loop Models for Risk Mitigation HITL models provide critical oversight during AI deployment. This process builds trust in AI systems and supports outcomes that align with ethical and organizational standards By integrating ethical and compliant practices into governance frameworks, leaders position their organizations to innovate responsibly while addressing emerging risks. Prepare for the Future AI is not static — it evolves rapidly, and governance frameworks must do the same. Leaders should prepare their organizations to adapt to future advancements in AI by prioritizing iterative governance models and continuous improvement. Human-in-the loop is a best practice; it requires upfront investment, but it drives continuous improvement by using human review to produce reliable, ethical and accurate outcomes. HITL governance models are particularly effective during the early stages of AI adoption, where human oversight can validate AI outputs and support alignment with organizational objectives. To future-proof AI governance frameworks, leaders should: Embrace iterative governance models: These frameworks evolve alongside AI advancements, allowing organizations to respond to emerging risks and opportunities Invest in automation: Building an AI automation process improves efficiency and reduces manual oversight, supporting scalability over time -- allowing iterative involvement from different business units to provide benefits across a company By focusing on adaptability, governance frameworks remain relevant and effective, even as AI technologies and use cases expand. Evaluate Your AI Governance Maturity Effective AI governance requires continuous evaluation to keep pace with evolving risks, regulations and business needs. Organizations should assess their current frameworks to identify gaps, support compliance and align AI strategies with long-term goals. Optiv provides tailored governance solutions to help organizations strengthen their AI security posture, mitigate risks and enable responsible innovation. Contact Optiv today to assess your AI governance maturity and develop a governance strategy that balances security, compliance and business growth. By: Brian Golumbeck Director, Strategy and Risk Management | Optiv Brian Golumbeck is a Practice Director within Optiv Risk Management and Transformation Advisory Services Practice. He has a history of leading challenging projects and building dynamic high impact teams. Mr. Golumbeck’s 25+ years working in Information Technology, include 20+ years as an information security professional. Brian is a Certified Information Systems Security Professional (CISSP), Certified in Risk and Information Systems Controls (CRISC), Certified Information Security Manager (CISM), Certificate of Cloud Security Knowledge (CCSK), EXIN/ITSMf ITIL Foundations, and Lean Six Sigma – Greenbelt. 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