AI and the difference between safety, security and business risk [Q&A]

The rapid ascent of artificial intelligence across industries has ushered in a new era of innovation, but it has also introduced a labyrinth of security challenges that many organizations are only beginning to grasp. As AI systems become more deeply embedded in decision-making processes and customer interactions, the stakes for ensuring their safety and integrity have never been higher. To shed light on these pressing concerns, we sat down with Dr. Peter Garraghan, CEO and co-founder of Mindgard, a pioneering company specializing in AI security testing.

Dr. Garraghan brings a wealth of expertise to the table, having spent years at the intersection of AI research and enterprise security. His insights reveal that the integration of AI into business operations is not just a technological shift—it’s a paradigm shift that demands a reevaluation of traditional security frameworks.

BN: What unexpected problems has the rollout of AI tools created for businesses?

PG: AI behaves very differently than traditional software, and most existing security tools simply weren’t built for that. AI learns patterns, interprets prompts, and makes decisions in ways that can be opaque even to its creators. This introduces a host of new vulnerabilities that traditional cybersecurity measures are ill-equipped to handle.

For instance, AI systems can be manipulated through adversarial attacks, where subtle changes to input data can cause the system to behave in unintended or harmful ways. Imagine a self-driving car misidentifying a stop sign due to a carefully crafted sticker, or a financial AI system being tricked into approving fraudulent transactions. These are not hypothetical scenarios—they are real risks that businesses must contend with.

Moreover, the data used to train AI models can itself be a liability. If the training data is biased or compromised, the AI’s decisions will reflect those flaws, potentially leading to reputational damage, regulatory fines, or even legal action. And because AI systems often operate as black boxes, diagnosing and fixing these issues can be incredibly challenging.

Another layer of complexity comes from the fact that AI is increasingly being deployed in customer-facing roles. Chatbots, recommendation engines, and virtual assistants are now commonplace, but they also represent new attack surfaces. A compromised AI could leak sensitive customer data, spread misinformation, or even be weaponized to manipulate public opinion.

BN: How can enterprises address these risks?

PG: The first step is recognizing that AI security is not just a technical issue—it’s a business issue. Companies need to adopt a holistic approach that encompasses technology, processes, and people. This means investing in specialized AI security tools, like those developed by Mindgard, which are designed to identify and mitigate risks unique to AI systems.

But technology alone isn’t enough. Organizations must also establish clear governance frameworks for AI, including policies for data quality, model transparency, and incident response. Regular audits and stress tests can help identify vulnerabilities before they’re exploited. And perhaps most importantly, businesses need to foster a culture of security awareness, ensuring that everyone—from developers to executives—understands the risks and their role in mitigating them.

BN: What’s the biggest misconception about AI security?

PG: The biggest misconception is that AI security is just an extension of traditional cybersecurity. While there are overlaps, AI introduces entirely new challenges that require specialized expertise and tools. For example, traditional penetration testing might focus on finding bugs in code, but AI security testing needs to account for the dynamic, probabilistic nature of AI systems.

Another misconception is that AI is inherently neutral or objective. In reality, AI systems are only as good as the data they’re trained on, and that data can carry biases or inaccuracies. Businesses need to be vigilant about the ethical implications of their AI deployments, not just the technical ones.

BN: Looking ahead, what trends do you see shaping the future of AI security?

PG: One major trend is the growing emphasis on explainable AI—systems that can provide clear, understandable rationales for their decisions. This is crucial for building trust and accountability, especially in high-stakes applications like healthcare or finance.

Another trend is the rise of AI-specific regulations, such as the EU’s AI Act, which will require businesses to demonstrate that their AI systems are safe, transparent, and fair. Compliance will become a key driver of AI security practices.

Finally, I expect to see increased collaboration between academia, industry, and government to address the complex challenges of AI security. No single entity has all the answers, but by working together, we can build a safer, more resilient AI ecosystem.

In conclusion, the integration of AI into business operations is a double-edged sword. While it offers unprecedented opportunities for innovation and efficiency, it also introduces new risks that demand a proactive, multifaceted approach to security. By staying informed, investing in the right tools, and fostering a culture of vigilance, enterprises can harness the power of AI while safeguarding against its potential pitfalls.

Tags: AI security, enterprise risk, artificial intelligence, cybersecurity, adversarial attacks, data bias, explainable AI, AI governance, regulatory compliance, Mindgard, Dr. Peter Garraghan, black box AI, customer-facing AI, ethical AI, AI Act, AI testing tools, AI vulnerabilities, business innovation, AI safety, AI transparency.

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