Synthetic intelligence (AI) and automated learning (ML) have made their way into the corporate environment.
Per the IBM AI in Action 2024 Report, two main categories are adopting AI: Visionaries and scholars. Visionaries are observing quantifiable outcomes, with two-thirds reporting a 25% (or greater) rise in revenue growth. Scholars, on the other hand, mention that they are following an AI strategy (72%), but only 40% indicate that their top management fully comprehends the significance of AI investment.
One common aspect among them? Issues with data security. Despite their achievements with AI and ML, security remains the primary concern. Here’s the reason behind this.
In full motion: How AI and ML enhance
In the past, computers executed commands as programmed. Thinking creatively was not an option — codes determined what was feasible and allowed.
AI and ML structures take a different stance. Instead of strict arrangements, AI and ML structures get general principles. Businesses provide ample training data that assist these structures in “learning,” which, in turn, enhances their performance.
For instance, an AI tool designed to recognize dog images. The foundational ML structures provide elementary pointers — dogs have four limbs, two ears, a tail, and fur. Numerous dog and non-dog images are fed to AI. The more images it processes, the more adept it becomes at distinguishing dogs.
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Off the beaten path: The dangers of unauthorized model alteration
If unauthorized parties gain entry to AI models, they can alter model outcomes. Consider the above example. Malignant manipulators breach business networks and inundate training models with non-categorized cat images and images wrongly labeled as dogs. Over time, model precision deteriorates, and outcomes become unreliable.
Forbes outlines a recent contest where hackers attempted to “jailbreak” prevalent AI models and deceive them into producing imprecise or harmful material. The upsurge of generative tools accentuates the necessity for such protection — in 2023, researchers disclosed that simply appending random symbol strings at the end of queries could persuade generative AI (gen AI) tools to present answers bypassing model safety checks.
This apprehension is not merely theoretical. As highlighted by The Hacker News, a hacking ploy known as “Sleepy Pickle” poses substantial threats for ML models. By embedding a malicious payload into pickle files — used to serialize Python object frameworks — perpetrators can alter how models evaluate and contrast data, subsequently modifying model results. This could enable them to fabricate misinformation that brings harm to users, pilfer user data, or produce material containing malevolent web links.
Standing firm: Three constituents for enhanced security
To minimize the risk of jeopardized AI and ML, three elements are crucial:
1) Safeguarding the data
Precise, prompt, and consistent data serves as the foundation for practical model results. Yet, the procedure of centralizing and correlating this data presents an appealing target for attackers. If they manage to infiltrate large-scale AI data repositories, they can adjust model outputs.
As a result, companies require solutions that automatically and persistently monitor AI infrastructure for signs of compromise.
2) Safeguarding the model
Modifications to AI and ML models can lead to results that look valid but have been altered by intruders. At best, these results inconvenience customers and impede business processes. At worst, they could adversely affect both reputation and revenue.
To minimize the risk of model manipulation, entities necessitate tools capable of recognizing security loopholes and detecting misconfigurations.
3) Safeguarding the utilization
Who is utilizing the models? With what data? And for what objective? Even if data and models are safeguarded, malicious utilization may put companies at risk. Continuous adherence monitoring is crucial to ensure legitimate utilization.
Capitalizing on the potential of models
AI and ML tools aid companies in uncovering data insights and enhancing revenue. Nonetheless, if compromised, models can be utilized to deliver inaccurate outcomes or deploy malevolent actions.
With Guardium AI security, businesses possess enhanced capabilities to address the security threats associated with sensitive models. Explore more.