In today’s enterprise landscape, artificial intelligence is no longer just a background tool; it has become a transformative force. However, for internal audit teams, the rise of AI presents a pressing challenge: keeping up with technology that advances faster than traditional governance models. The “2025 Focus on the Future” report from AuditBoard highlights this issue: 61% of internal audit leaders admit to lacking AI expertise, leaving organizations vulnerable to risks that can develop and escalate undetected. Failures in AI oversight aren’t hypothetical; they can cause operational disruptions, regulatory scrutiny, and financial losses.
In that context, there are two dangers for organizations: first, understanding the special risks that AI systems introduce, and second, embedding defence into processes built for static, predictable environments. Typical audits, point-in-time ones that they are, just cannot spot algorithmic errors that are highly intricate in nature, nor can they identify the vulnerabilities that could evolve into operational issues. An unguarded situation, therefore, invites operational risks and reputational consequences that come with choosing not to scrutinize decisions generated by AI.
Within this environment, Anand Salodkar, Senior Manager of Internal Audit at a global leader in media and audio innovation, has taken a markedly forward-thinking stance. Rather than relying on conventional audit protocols, he has designed an AI-specific risk assessment framework that combines classical audit rigor with machine learning governance. This approach is not reactive; it anticipates risks, flagging vulnerabilities before they grow into serious exposures, filling a critical void in AI risk management.
“Audit shouldn’t just be about looking at what went wrong,” Anand reflects. “It should be about anticipating what could go wrong and proactively safeguarding the organization against those risks.”
Traditional SOX audits are much more in-the-affirmative; they follow a set checklist and work retrospectively. Anand’s framework keeps monitoring operational data to churn out insights in real-time. Within the organization, this approach was found to reduce risk exposure by 30% and reduce audit cycle times by 15%, so this approach actually marries operational excellence with better security. More fundamentally, it champions a culture wherein proactive vigilance rules, changing the very mindset with which audit teams look at organizational processes.
His work represents a significant shift in mindset. It moves audit from a function perceived as compliance policing to a strategic enabler, demonstrating that risk management can drive business performance. Through predictive analytics, his team stays better in tune with what can go wrong before such matters hinder operational matters. Armed with such evidence, the management can definitively take action to retain continuity in a context of a crisp AI-centric landscape.
Anand’s impact transcends the organization of which he is a member. As a Future Impact Group Fellow, he works on AI governance policies that have been adopted by institutions such as Oxford University and Google DeepMind. Through events such as the Wharton AI Conference 2024, he creates sufficient linkages to bring insights from his organization into the broader industry discourse. He sets standards for ethically oriented and responsible deployment of AI
His framework offers a blueprint for cross-industry applications. Continuous monitoring and predictive risk assessment principles can be adapted to finance, healthcare, and technology. These principles guide organizations through AI-driven complexity. By integrating predictive analytics into routine audit workflows, he demonstrates that technical rigor and strategic foresight can coexist, creating operationally viable solutions with wide relevance.
Furthermore, the method offers a replicable framework for companies that want to improve their internal security measures but do not want the security to have a negative impact on innovation. Because of its flexibility, it can be combined with current systems and still hold up to a progressive view that is able to foresee new kinds of risks. This is an especially important feature for firms that are putting a lot of resources into AI-based products and services.
Recognition has naturally followed. Membership in the Visual Effects Society underscores the interdisciplinary significance of his AI audit methodology. Alignment with emerging professional accounting standards signals influence on evolving audit practices. Anand’s broader contributions demonstrate that he is not merely keeping pace; he is redefining how organizations conceptualize and manage AI risks. Corporate leaders and professional bodies increasingly mirror the proactive approach his work encourages.
“The goal is not just to audit AI, it’s to ensure that AI itself supports the organization responsibly, securely, and efficiently. If we can achieve that, the potential impact across industries is enormous,” Anand emphasizes.
With artificial intelligence now in full-speed adoption, there is an increasing demand for predictive audit functions. This framework by Anand is an example of how internal audit can ride through a transition from oversight to foresight. Anand’s work provides an actionable framework for other organizations, illustrating the opportunity that can arise when governance meets innovation.
Significantly, his methodology addresses a persistent tension between compliance and innovation. His framework, by embedding predictive analysis into audit procedures, illustrates that governance may facilitate rather than obstruct strategy. This signals a shift at the level of the profession. In the future, audit leaders would have to be conversant in both technology and risk strategy in steering through complex AI-driven enterprise environments.
In essence, Anand Salodkar’s contributions go beyond improving processes; they define a new paradigm for internal audit. Through technology-aided insight, he equips organizations to confidently anticipate risk, thus securing operational and strategic outcomes. His work has made way for a site of replication in the industry: anticipation, adaptability, and proactive governance would be the way forward in an age with pervasive AI.
The broader implication is clear: the integration of AI-focused audit frameworks is not a niche advancement; it is becoming a necessity for modern enterprises. By emphasizing proactive risk assessment, Anand demonstrates that audit can be a source of competitive advantage, ensuring resilience and operational excellence even in the face of technological disruption. As more organizations grapple with the challenges of AI adoption, frameworks like his, may well set the standard for how internal audit contributes to sustainable, responsible growth.
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