Jun 12, 2024




Jason Albuquerque

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Imagine you’ve done the impossible and developed a limitless energy source in a nice compact package. Dreams of limitless and clean power begin to flood your thoughts. This will revolutionize the world. But with that, worry and doubt begin to fester. How do you contain such immense power?

This is the challenge of deploying artificial intelligence. AI offers incredible power, from streamlining operations to personalizing customer experiences. But an unrefined AI system fueled by insecure data and systems can create serious problems.

Imagine a threat actor infiltrating your AI system. Sensitive customer data could be exposed, leading to lawsuits and a public relations disaster. Biased data leads to biased AI. Think of a hiring algorithm that unintentionally discriminates based on ZIP code, denying opportunities to deserving candidates. Malicious actors could manipulate your AI, feeding it false information to false narratives and introducing conflict.

These are merely a few risks of so many that we face. Deploying AI without robust data security and privacy measures is like messing with an unstable limitless energy source in your basement. It’s a recipe for disaster.

Deploying AI without robust data security and privacy measures is like messing with an unstable limitless energy source in your basement. It’s a recipe for disaster.


So, how do we unlock the true potential of AI while addressing these risks? This is where the experience and insight of the cybersecurity community come to the rescue. Frameworks such as the Open Worldwide Application Security Project Top 10 for Learning Machines & Lesser Models offer a blueprint for secure AI. These guidelines help identify and address vulnerabilities in AI systems and help ensure that these systems stay secure and trustworthy.

Some of these major risks are things such as sensitive information disclosure. Best practices and guidance around data minimization, information protection and access controls, ensure only authorized information reaches the AI ecosystem.

Insecure functionality design could introduce major risks that fly under the radar. Plugins are additional functionalities that can be added to your AI ecosystem. Poorly designed plugins can introduce weaknesses in the system. Best practice calls for secure coding practices and rigorous testing of these additional functionalities.

Unstable output handling can undermine our ability to trust AI. Just because a malfunctioning sensor displays a warning doesn’t mean it’s accurate. This vulnerability allows malicious actors to manipulate the AI’s output. Focusing on quality of output validation and context awareness ensures the AI’s output is reliable and trustworthy.

Another key risk: overreliance. We should never put all our eggs in one basket. Overreliance on a single AI model can be disastrous. Utilizing diverse models and human oversight creates a more robust and secure AI ecosystem.

On top of this, let’s ensure that we are championing transparency. Be upfront with your customers about how their data is used and empower them with control over their information. Ensuring clear communication about how your AI tools arrive at their conclusions.

Remember, security for our systems, new and old, isn’t a one-time fix but a continuous journey. Prioritize regular security assessments of your data and systems, just as you would for your network infrastructure so that you understand the risks and can promptly remediate them.

The AI revolution promises a future that is incredibly bright, but a secure foundation is crucial. By proactively addressing data security and privacy, we ensure AI becomes a tool for innovation, not a source disruption. The choice is ours.  Will you invest in the necessary safeguards before unleashing the power of AI, or will you gamble with a system on the verge of overload?