The rapid rise of artificial intelligence (AI) has transformed the way businesses operate, offering significant opportunities for innovation, efficiency, and value creation. However, with this transformation comes a new set of security challenges. Organizations are finding that AI not only introduces new threats but also creates opportunities for enhancing security in ways that were previously unimaginable. At this week’s AI Realized Summit in San Francisco, a panel of experts including Jake Martens, CISO of Aristocrat, Liz O’Sullivan, CEO of Vera.ai, Vrahesh Bhavsar, CEO of Operant AI, and Shana Simmons, Chief Legal Officer at Zendesk, explored how AI security can be integrated into a broader business strategy, turning it from a defensive measure into a value-driving initiative.

Shifting the Security Paradigm: From Risk Minimization to Value Creation

Traditionally, security has been viewed as a cost center—a necessary function to protect a business from risks and threats but one that doesn’t directly contribute to revenue or growth. However, as Jake Martens pointed out during the panel discussion, that perspective is changing. “My strategy at Aristocrat was not just to minimize risk as it relates to cyber but to maximize value,” he explained. In today’s AI-driven world, security can serve as a differentiator that enhances trust, fosters customer loyalty, and strengthens a company’s market position.

AI security is no longer just about preventing data breaches or stopping malicious actors. It’s about creating an environment where innovation can thrive without being derailed by security failures. For companies deploying AI, demonstrating a robust security framework can build trust with customers and partners, especially in industries where data privacy and protection are paramount. This trust can become a unique selling point, differentiating companies from competitors who may not prioritize AI security in the same way.

Proactive Security: Addressing the Systemic Nature of AI Risk

One of the key themes from the AI Realized panel was the need to address the systemic risks introduced by AI. AI technologies, particularly large language models (LLMs) and machine learning systems, have the potential to create vulnerabilities that are fundamentally different from traditional security risks. These systems can introduce new attack vectors, such as model poisoning, where malicious actors insert harmful data into an AI model, leading to flawed decision-making or even catastrophic failures.

The panelists emphasized that AI security must be approached from a systemic perspective, integrating security measures across the entire enterprise. As Martens noted, it’s not enough to think of AI security in isolation. Security teams need to rethink their entire approach, considering the implications of AI on people, processes, and technology. This involves reevaluating existing security programs, adapting them to the unique challenges posed by AI, and ensuring that security is embedded into every stage of AI development and deployment.

Collaboration Across Departments: Breaking Down Silos

AI security is not solely the responsibility of cybersecurity teams. As Shana Simmons pointed out, AI security is a systemic risk that affects every department in an organization, from legal and compliance to marketing and product development. “Security is now all of our obligations,” she remarked during the panel. For AI security to be effective, it requires collaboration across the entire organization.

One of the key challenges highlighted by the panel is that different departments often have varying perspectives on AI risks. For example, while a security team may focus on technical vulnerabilities, a marketing department may be concerned with reputational risks if an AI-driven chatbot were to produce offensive or misleading content. Similarly, legal teams are increasingly focused on issues related to data privacy and compliance, particularly with regulations like the upcoming EU AI Act.

Breaking down these silos is critical to building a comprehensive AI security strategy. By fostering collaboration and communication between departments, organizations can ensure that AI risks are addressed holistically. This approach not only strengthens security but also ensures that AI technologies are aligned with broader business goals, including customer satisfaction, brand reputation, and regulatory compliance.

The Role of Data Governance in AI Security

One of the most significant risks in AI security is related to data. AI systems are highly dependent on the quality and integrity of the data they process. As such, robust data governance is essential for mitigating AI security risks. During the panel, Liz O'Sullivan, CEO of Vera, emphasized the importance of ensuring that data used by AI systems is accurate and free from malicious tampering. “It’s all about data governance,” she said, stressing the need for strong collaboration between cybersecurity and data teams.

Model poisoning, where attackers corrupt the data used to train or fine-tune AI models, can have disastrous consequences. For example, a financial institution using an AI model for credit scoring could be exposed to significant risks if the model were trained on poisoned data, leading to inaccurate credit decisions. To mitigate this risk, organizations must implement strict data validation and monitoring processes, ensuring that the data fed into AI models is trustworthy and secure.

Turning AI Security into a Competitive Advantage

Forward-thinking companies are not just viewing AI security as a way to mitigate risk—they are using it to drive business value. As Martens and other panelists highlighted, companies that prioritize AI security can differentiate themselves in the market by building trust with customers, partners, and regulators. In industries where data privacy, compliance, and trust are critical, having a strong AI security framework can be a key factor in winning new business and maintaining customer loyalty.

Companies like Salesforce and Cisco are already positioning AI security as a central part of their value proposition. By demonstrating their commitment to safeguarding AI systems, these organizations can market themselves as leaders in AI innovation and security, further enhancing their competitive advantage.

In a separate panel discussion at the summit, Matt Maccaux, head of customer engineering at Google Cloud compared AI governance to “eating your vegetables.” While many people don’t enjoy it, the process will make your organization stronger. Maccaux continued: “the most successful AI projects will come from companies that have eaten their vegetables.

Conclusion: AI Security as a Strategic Imperative

As AI continues to revolutionize industries, the need for robust AI security frameworks has never been more urgent. However, the conversation has evolved beyond simply mitigating risk. Today, AI security is a strategic business imperative—one that can create value, drive innovation, and build trust with customers and stakeholders.

At AppSOC we strongly endorse the idea that AI security should be viewed as both an enabler and accelerator. By taking a proactive approach to AI security, integrating it across the entire organization, and focusing on data governance and collaboration, companies can turn AI security into a competitive advantage. In this new AI-driven world, those that lead in security will not only protect themselves from risks but also position themselves as leaders in trust, innovation, and value creation.