Artificial intelligence (AI) is driving advancements in every industry and function. From healthcare to finance, AI helps organizations unlock new efficiencies, better understand their data and improve their competitive edge. But as exciting as AI is, it comes with significant security risks. If you’re implementing AI solutions, protecting these systems is critical—not just for your organization’s success, but to safeguard the sensitive data they handle.
Next week, our DevSecOps Virtual Summit on Sept. 24 will explore this in depth during the track on “Securing AI: Navigating the Risks and Mitigating Threats.” This topic couldn’t be timelier. As AI becomes more powerful, adversaries are also using AI to act smarter. Let’s take a closer look at the specific threats AI faces and how you can address them without slowing down progress.
Dangers Hidden in AI
AI is built to learn and adapt, which is exactly what makes it so valuable. But this same strength makes it vulnerable to attacks. AI systems can be manipulated in ways that were unimaginable just a few years ago. Adversarial attacks can introduce subtle changes to data to mislead AI models into making wrong recommendations. Imagine a financial AI that’s tricked into approving fraudulent transactions or a healthcare AI that misdiagnoses a patient due to tainted data.
These types of attacks are happening now. As AI becomes more widely used, they’ll only increase. Protecting AI isn’t just about technology — it’s about protecting the trust that both users and customers place in your organization.
But the question remains: Can AI be secured without compromising its potential?
The Growing Pressure to Secure AI
The pressure to safeguard AI isn’t just due to increased risks. It’s also coming from regulators. With the implementation of privacy laws such as GDPR and CCPA, organizations are responsible for protecting the sensitive data that feeds AI models. Failing to do so can lead to hefty fines and damaged reputations.
Striking the balance between security and compliance on the one hand and innovation and speed on the other is a challenge every organization faces. And this is where the debate comes in: Is it possible to innovate with AI and meet security standards at the same time?
The answer is “yes,” but it requires a deliberate approach. Organizations must take AI security seriously from Day 1, rather than viewing it as an afterthought. It also means working together across teams — security, development and legal — to ensure everyone’s aligned on how to deploy AI responsibly.
Practical Strategies for Securing AI
During the Sept. 24 summit I mentioned above, OpenText™ will walk through several strategies for protecting AI systems. Here’s a sneak peek:
- Build Strong Data Foundations: AI is only as good as the data it’s trained on. Verifying data accuracy and integrity helps prevent malicious actors from feeding your AI misleading information. It’s about ensuring the right controls are in place to check data quality every step of the way.
- Harden Your AI Models: Security must be baked into the AI model from the ground up. This means incorporating adversarial training during development, where models are tested against potential attacks. This helps build more robust systems that can withstand real-world threats.
- Monitor Continuously: AI security isn’t a “set it and forget it” process. Once your models are deployed, they need constant monitoring. Look for any unusual behavior or unexpected outcomes that could signal an attack. Early detection is key to minimizing damage.
- Cross-Team Collaboration: AI security isn’t the job of one department. It requires close collaboration between development, data science, legal and security teams. Working together can help ensure that AI systems are secure, compliant, and performing at their best.
How OpenText™ DevOps Aviator can Help
For many organizations, securing AI can be a daunting task. The OpenText DevOps Aviator product aims to simplify this process. DevOps Aviator integrates security into your AI workflows without slowing down your development process:
- Automated Security Checks: DevOps Aviator automates security reviews at every stage of AI development, making it easier to identify and fix potential vulnerabilities before they become an issue.
- Real-Time Threat Detection: With DevOps Aviator, you can monitor your AI models in real-time, detecting anomalies and responding to threats quickly.
- Compliance Made Simple: DevOps Aviator includes built-in tools to ensure your AI systems stay compliant with regulations such as GDPR and CCPA, allowing you to focus on innovation, not paperwork.
By using DevOps Aviator, you can overcome one of the biggest challenges in AI — how to maintain the pace of innovation while staying secure. It’s a solution that empowers your teams to adapt quickly without risking security.
The Future of AI Security Starts Here
AI is here to stay. The organizations that succeed will be the ones that find a way to balance security with innovation. Securing AI doesn’t mean stifling progress — it means protecting the systems that will define the future of technology.
The DevSecOps Virtual Summit will dive into these challenges and provide practical guidance on how to protect your AI investments. Don’t miss this opportunity to learn how to secure your AI systems while keeping your innovation moving forward.
Join us on Sept. 24 to explore these crucial topics and take the first step toward securing your AI future.
