#148 The Controversy Surrounding Generative AI: Theft or Technological Progress?
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AI BYTE # 📢: The Controversy Surrounding Generative AI: Theft or Technological Progress?
Generative AI, a marvel of modern technology, has been making waves in various industries due to its innovative capabilities. However, it’s not without controversy.
One of the most pressing concerns is whether generative AI is built on theft. This concern arises from the fact that AI models are fed large amounts of copyrighted material during their training process, leading to worries that the resulting output may unknowingly reproduce copyrighted content.
While traditional methods of identity theft primarily relied on hacking databases or phishing emails, Generative AI introduces an even more insidious element. This technology has the potential to improve, simplify, and automate many things. However, these potential benefits come with a cybersecurity overhead that can be more complex to deal with than it seems.
A world using Generative AI technology has the potential to unlock many benefits. At the same time, it introduces significant security challenges. The list of major security and privacy concerns when an organization adopts Generative AI technology is extensive, and includes sensitive information disclosure, data storage compliance, information leakage, model security vulnerabilities in Generative AI tools, bias and fairness, transparency, trust, ethics, infringement of intellectual property and copyright laws, deepfakes, hallucinations (nonsensical or inaccurate), and malicious attacks.
There is plenty of information available on these concerns and on the proactive measures that organizations can take to address them. Typically, these measures include the creation of organizational policies, data anonymization, the principle of minimum privilege, threat modelling, preventing data leaks, secure deployment, and security audits.
In the face of these challenges, organizations are adopting a holistic approach to Generative AI security. This encompasses the entire AI lifecycle, including data collection and handling, model development and training, and model inference and use.
Simultaneously, they secure the infrastructure on which the AI model was built and run. Finally, they establish an AI governance process in the organization.
Practical measures include establishing an Acceptable Usage Policy (AUP) for Generative AI tools. The purpose of the policy is to ensure that employees use Generative AI systems in a manner that is consistent with the organization’s values and ethical standards. For example, the policy may state that employees should not submit any personally identifiable information (PII) data or copyrighted materials to Generative AI tools.
Data security is another crucial aspect. To secure the data at rest and in transit, organizations start with a data discovery and classification process to establish the sensitivity of data and determine which data should go into the Generative AI model.
To protect sensitive data in the training data sets, they anonymize sensitive data, encrypt the data at rest and in transit with strong encryption algorithms to ensure the confidentiality of data, and restrict access to AI training data sets and the underlying IT infrastructure by implementing access controls within the enterprise environment.
Despite these measures, the potential for misuse of Generative AI is significant. Deepfakes, voice fakes, and text fakes are becoming increasingly sophisticated.
Our data spreads like pollen and, unfortunately, you cannot wipe it from the interwebs. Your PII is out there, ready to be used against you.