Tackling the maze of AI cyber risks requires navigating a complex landscape of
vulnerabilities, from adversarial attacks and data poisoning to evolving supply chain threats. By adopting proactive strategies and building resilient systems, organizations can stay one step ahead in safeguarding their AI-driven technologies.

Fortifying AI: Resilience Meeting Security

Guardrails for the Cyber Frontier. The rise of AI introduces new cybersecurity vulnerabilities that can threaten the integrity of AI-driven systems, making security a top priority. The good news? By identifying these challenges early and taking proactive steps, organizations can build robust defenses to secure their AI systems against emerging threats.

In this article, we’ll delve into the most pressing AI cybersecurity risks and share practical strategies to help you fortify your AI systems, ensuring they stay resilient in the face of constant evolution.

First Line of Defense: Secure Training Data

Protect your learning foundation like a fortress. AI systems rely heavily on data—but what happens when attackers mess with that data? Data poisoning is exactly that: hackers insert false, malicious, or corrupted information into AI training data used to create AI models, causing the system to behave unpredictably leading to flawed outcomes.

What’s the best way to handle this? Think of your training data like food prep—keep it clean and well-monitored. Implement tools to validate the data going into your system so you know it’s free of manipulation. It’s important to always be on the lookout for anomalies in your datasets—especially if something feels off, trust your instincts, and investigate. And last, be sure to train your AI with fresh, trusted data on a regular basis to keep your training models accurate and reliable.

The Silent Saboteurs: Small Changes, Big Consequences

Let’s talk about AI’s blind spot. Here’s where things get tricky. Adversarial attacks involve feeding AI slightly manipulated inputs—small, almost imperceptible changes to input data that confuses AI systems, leading them to make wrong decisions. This type of attack exploits vulnerabilities in AI systems, and can threaten their security, reliability, and integrity. Think of it like handing an AI a puzzle piece that looks like it fits but doesn’t.

How do you stop this? The answer is you need to toughen up your AI models. Start by training them on examples of these types of attacks—a practice known as adversarial training. When your system learns what to watch for, it will be less likely to fall for the trick. Regular stress testing is also key. Keep putting your AI through its paces so you can spot and patch weaknesses before attackers can exploit them.

Cracks in the Code: AI Under Attack

Securing AI’s most sensitive information. AI models often handle huge amounts of personal or sensitive data, which makes them an attractive target for hackers. When cybercriminals get their hands on this data, you have a data breach that could lead to anything from identity theft to massive leaks of proprietary information. It’s like putting all your valuables in one safe—if someone breaks in, they get everything.

The mitigation? The first thing you want to start with is encryption—lock your data up tight whether it’s stored or in transit. Role-based access control can also help by limiting who can interact with sensitive data—only those who absolutely need access should have it. And don’t skip the regular security audits; they’re your best shot at finding vulnerabilities before someone else does. Finally, make sure your AI integrates securely with other systems—weak links are often where breaches happen.

Shine a Light on Bias: Prioritize Real Threats

Clean data, clear decisions. AI might seem impartial, but here’s the truth: it’s only as good as the data it learns from. If your training datasets contain information with bias and discrimination, your AI might start making unfair decisions. In cybersecurity, this could mean failing to prioritize real threats or generating too many false positives—which wastes valuable time and resources.

The fix? Start with diverse, representative training data—your AI can only be fair if the data is fair. Run regular bias audits to check for any unintended patterns or discriminatory outputs. When possible, use explainable AI tools to understand why your AI is making certain decisions. And don’t leave it all to the machines: having human oversight helps to ensure the AI stays on track.

Don’t Fall for the Trick: Outsmart Social Engineering Attacks

Guard your mind, guard your data. AI has improved the ability of threat actors to create highly personalized and more sophisticated content than we’ve seen before. Imagine AI-powered phishing campaigns that generate ultra-personalized messages or deepfake videos that impersonate trusted individuals. These advanced social engineering attacks are more convincing than ever, making them much harder to detect.

Ready to fight back? Put AI on your side—behavioral analytics tools powered by AI can detect unusual activity and flag it as a potential attack. Multi-factor authentication is another must-have—even if someone steals credentials, MFA can stop them in their tracks. It’s also worth investing in identity verification tools to root out impersonators. And don’t forget the human element—ongoing employee training is critical to spot phishing and other social engineering tricks.

Secure Every Link: Fortify the Supply Chain

Build strong, build secure. AI systems often rely on third-party tools, libraries, and datasets. The problem? These external components might contain hidden supply chain vulnerabilities that attackers can exploit. If one part of your AI supply chain gets compromised, the entire system could be at risk—like dominoes falling one after another.

Here’s how to tackle it: Vet every vendor and third-party tool you bring into your ecosystem—you wouldn’t invite someone into your house without checking who they are first, right? Version control is also essential to track and secure the components your AI depends on. For added transparency, maintain a software bill of materials to document everything in your AI stack. Finally, keep a close eye on your supply chain with continuous monitoring—the faster you spot an issue, the faster you can fix it.

Stay Ahead: Significance of AI Risk Management

It’s a matter of necessity. Addressing AI risks is critical because failure to do so can have far-reaching consequences. Vulnerabilities like data poisoning or adversarial attacks can make AI models unreliable, leading to missed threats or incorrect decisions. Data breaches, on the other hand, can expose sensitive information and damage an organization’s reputation. By staying vigilant and proactively tackling risks, organizations can ensure their AI models remain accurate, resilient, and trustworthy. Strong security measures also prevent cybercriminals from hijacking AI tools for their own malicious purposes. Beyond that, businesses that prioritize AI security are more likely to stay compliant with rapidly evolving regulations and ethical standards. Taking these steps now saves time, money, and credibility in the long run. Ultimately, staying ahead in AI security will require constant adaptation, innovation, and collaboration across industries. As AI continues to grow, so too must our strategies for protecting it.

If you liked this article, you might also like:

• Maximizing the full potential of AI with performance and scalability Read article
• Revolutionizing the AI landscape with retrieval-augmented generation (RAG), knowledge graphs, and quantized large language models (LLM) Read article

Special thanks to the contributors and technical reviewers of this article:

• Hugh Bolton, President & Facility Security Officer
• Peyton Rosenberg, Information Systems Security Manager
• Janette Steets, PhD, Director of Data Science
• Cathy Claude, Director of Marketing

Careers