May organizations use generative AI as an innovative and useful tool to help enhance and grow their businesses. As accessible as generative AI is, the risk of misuse is high, as employees may copy sensitive information, such as PII, source code, and trade secrets, into the tools.
AI-R DLP (AI-Radar Data Loss Prevention) prevents information leaks that may occur when using generative AI. Using pattern-matching and AI technology, sensitive information can be detected more accurately, making it possible to identify or apply post-processing policies to data used in generative AI. Administrators can set a policy to block sensitive information, increasing work productivity and building a safe generative AI environment.
By utilizing existing pattern-matching methods and AI technology, you can monitor the data used in generative AI and detect and block sensitive information. This helps prevent situations where a public generative AI tool learns important company information due to user mistakes.
Administrators can implement policies to block users from uploading sensitive information to public models.
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Learn how AI-R DLP accelerates your AI journey without putting your data at risk. Fasoo effectively addresses data privacy concerns and mitigates the risk of data leaks in generative AI.
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How do you mitigate data security and privacy issues in this new AI world? Learn how Fasoo addresses the challenges of using generative AI responsibly.
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Learn how AI-R DLP helps organizations manage the risks of using AI while enjoying the benefits. Understand the limitations of AI tools to build a secure generative AI environment.
Fasoo AI Radar DLP, or Fasoo AI-R DLP, is an AI-ready security solution that prevents inadvertent data leaks in generative AI by blocking prompts that contain sensitive data.
AI security can refer to two different instances. First is the practice of protecting artificial intelligence systems and the data they process from cyber threats, malicious attacks, and unauthorized access. This involves implementing measures to secure AI models, algorithms, and infrastructure, ensuring the integrity, confidentiality, and availability of AI-driven applications. AI security also encompasses safeguarding the data used to train and operate these systems, preventing data poisoning, model inversion, and other adversarial attacks. By fortifying AI systems against potential vulnerabilities, organizations can maintain trust in their AI capabilities and ensure reliable, safe, and ethical use of artificial intelligence technologies.
The other definition is the application of artificial intelligence technologies to enhance the protection of systems, networks, and data from cyber threats. AI Security involves using machine learning algorithms, pattern recognition, and other AI techniques to detect, prevent, and respond to security incidents more effectively. These technologies can identify anomalies, predict potential threats, automate responses, and improve the accuracy of threat detection by learning from vast amounts of data. By leveraging AI, organizations can enhance their cybersecurity measures, quickly adapt to emerging threats, and minimize the risk of cyberattacks, thereby ensuring the integrity, confidentiality, and availability of their digital assets.
