Imagine a security system that can understand not just text or images but a fusion of diverse data types - AI Multimodal. The fusion of AI and Multimodal capabilities has delivered a new era in cybersecurity. But within this innovation lies a mystery - How do we defend against Cyber threats that seamlessly blend across text, images, and audio?
Read below to understand more on the battlefield of cyber attacks, exploring the intricate dance between defence and offence.
Google just launched a new Artificial Intelligence (AI) last week on the 7th of December 2023. And here’s what you need to know about it.
Gemini is a Multimodal AI, which can process various data types such as image, text, speech, and numeral data. Those data are merged with many intelligent processing algorithms to enhance overall performance. In numerous real-life situations, multimodal AI frequently surpasses single-modal AI in performance.
Gemini comes in three sizes: Gemini Ultra for the most capable and largest model for highly complex tasks, Gemini Pro for a broad range of tasks, and Gemini Nano for on-device tasks. Based on their YouTube videos of Introduction to Gemini, Gemini covers these particular cases:
Computer vision encompasses the identification of objects, comprehension of scenes, and detection of abnormalities.
Geospatial Science involves the integration of various data sources, strategic decision making and ongoing surveillance activities.
Personalised healthcare, the integration of biosensors, and preventative medicine are all crucial aspects of human health.
Integrated technologies encompass various elements such as transferring specialized knowledge across different domains, combining data from multiple sources, improving decision-making processes, and utilising Large Language Models (LLMs) to enhance overall performance.
One word: Amazing. Right? Yes. Scary? Also yes. But, What does AI Multimodal impact on Cyber Attacks and the Cyber Security world? Let us tell you.
Adversarial Attacks: Attackers can exploit the complexity of AI Multimodal systems by crafting adversarial examples that manipulate multiple modalities simultaneously. For example, they might create multimedia content (images, audio, text) that can deceive AI systems, leading to misclassification or bypassing security measures.
Data Privacy Concerns: The use of various data types raises privacy concerns. Multimodal systems often require access to diverse and sensitive information, increasing the potential for privacy breaches or unauthorized access. We are sure, the last thing you want is your private information out there for the public to see. Yikes.
Complexity and Interpretability: Multimodal AI models are very complex, making them difficult to interpret. Understanding how decisions are made within these systems is crucial for security purposes, especially when dealing with cyber threats. Lack of interpretability can lead to vulnerabilities that attackers might exploit.
Ensuring the reduction of these risks is vital, and it can be achieved through continuous research and development in areas like adversarial robustness, techniques that preserve privacy, and the creation of AI systems that can be easily explained. To fully utilize the potential of AI Multimodal and protect against cyber threats, it is crucial to make AI systems strong enough to withstand adversarial attacks and implement strict measures to maintain data privacy.
Why not contact us for help and tell us more about your concerns?