Sahil Dhir has more than 14 years of experience in information security, governance and compliance. He is careful to note that in order to implement Generative Artificial Intelligence (GenAI) successfully, effective governance is needed. As organizations begin to adopt and experiment with AI technologies, Dhir encourages a holistic approach. He agrees that this needs to begin with a complete inventory of existing technological infrastructures.
He doesn’t think that successful GenAI deployment is just about adopting some new tools. It needs to be relevant to an organization’s unique goals, industry needs, and day-to-day organizational environment. This perspective is particularly relevant as businesses navigate the complexities of AI technology while maintaining ethical standards and security protocols.
Evaluating Existing Technologies
To start this process, Dhir emphasizes that the initial step to deploying GenAI is a careful analysis of an organization’s existing technology environment. He believes that knowing what already exists is key to understanding what’s missing, where the gaps are, and how to inform future improvements.
“Cybersecurity in the age of GenAI is about anticipating threats before they materialize,” he states. This forward-looking approach gives organizations far greater flexibility to develop AI governance frameworks that work best for them.
Informed by this evaluation, organizations can start developing and building functional governance models. These models will help transfer burden to different departments and set mechanisms of accountability clearly. This collaborative culture fosters an environment where different units can contribute to a shared understanding of AI’s role within the company.
Establishing Effective Governance Structures
Dhir emphasizes the importance of setting firm policies to govern data. This is critical to establishing the foundation for an effective AI governance framework. He calls on companies to introduce systems to track algorithmic decisions and set up protocols for dealing with ethical issues. These issues are deceptively simple, yet foundational keys to constructing a robust system. With this terrific investment in innovation comes a responsibility to protect against potential vulnerability.
“The key is developing governance structures that feel natural to the organization rather than imposed from outside,” Dhir explains. He argues that frameworks need to be based on a company’s specific values and how they operate, instead of taking a cookie-cutter approach.
Dhir touches on the need for human oversight in effectively adopting GenAI systems. He agrees with industry sentiment that AI-driven tools are best placed to increase operational efficiencies dramatically. Human judgment is absolutely vital to making sure that these technologies are addressed to your organization’s specific challenges and goals.
The Role of Cybersecurity in AI Implementation
In this context, as a scope management and risk manager, Dhir’s role assumes critical importance in the design and deployment of GenAI systems. This is why he proactively works to bake cybersecurity protections into a company’s DNA. This method increases transparency into agency decision-making processes and helps facilitate auditing.
On one hand, AI-driven tools are instrumental in helping detect and mitigate cyber threats through real-time monitoring and predictive analytics. Yet this technology could easily be weaponized. It leads to more dangerous, yet more sophisticated attacks such as deepfake scams and adversarial machine learning exploits. He warns. This dual-use nature of AI technology necessitates a well-structured governance framework that not only fosters innovation but mitigates risks associated with cyber threats.
Dhir encourages organizations to adopt flexible Governance, Risk Management and Compliance (GRC) frameworks that can grow and change as an organization’s needs do. This flexibility is key to helping them stay one step ahead of bad actors and proactively patch vulnerabilities.
