Generative â¤AI: Navigatingâ Privacy âand Security Challenges
Introduction
Generative AI tools, such as ChatGPT andâ Googleâs Gemini,⤠have revolutionized how we interact with technology.⤠However, their ability to âcapture and process vast amounts of data has raised significant privacy and security âconcerns.
Overexposed Data Risks
Sensitive Data Exposure
One of the⤠primary challenges of using generative⣠AI at work is the inadvertent exposure âof sensitive data. Camden Woollven, group head of AI âat GRC International Group, explains that most generative AI systems act like ââbig sponges,â absorbing⢠extensive information from the internet to train their language models.
Data Collection Concerns
Steve Elcock, âŁCEO and founder at Elementsuite, highlights that AI companies are â˘âhungryâ for data to train their modelsâ and often make it behaviorally attractive to share information. This extensive data collection poses the riskâ of sensitive information entering âsomebody elseâs ecosystem,â as noted by Jeff â˘Watkins, chief product âand technology officer at xDesign.
Hackerâ Threats
AI systems themselvesâ can be targets for hackers. Woollven warns â¤that if âan attacker gains access â˘to theâ large language model (LLM) powering a âŁcompanyâs AI âtools, they could siphon⤠off sensitive data, plantâ false outputs, or use theâ AI⤠to⤠spreadâ malware.
Proprietary AI Tools and⤠Privacy
Risks with Consumer-Grade⣠AI
Philâ Robinson,⣠principal consultant at Prism Infosec, points out⢠that even proprietary AI tools like â¤Microsoft Copilot can⣠pose risks. If access privileges are not properly⢠secured, employees might accessâ and leakâ sensitive data such as pay scales or M&A activity.
Employee Monitoring Concerns
AI âtools could also be used âto monitor staff, â˘potentially infringing on⤠their privacy. Microsoftâs Recall feature â¤claims that âyour snapshots are yours; they stay⢠locally on your PCâ and assures users of privacy control. However, Elcockâ suggests that it might not be long before such technology is used for employee monitoring.
Self-Censorship and Best Practices
Avoid Sharing Confidential Information
Lisa⣠Avvocato, vice president of⤠marketing and community⤠at Sama, âadvises against putting confidential information⣠into prompts for publicly available tools like ChatGPT or Googleâs â˘Gemini.â Instead,⢠use generic prompts and layer inâ sensitive information manually.
Validate AI Outputs
When using AI for research, âvalidate theâ information it provides. Avvocato recommends asking AI âŁto provide references⢠and links⢠to its sources and reviewing any⣠code it generates rather than assumingâ it is error-free.
Treat AI as a Third-Party Service
Woollven âŁemphasizes the importance of â¤treating AI like any other third-party service. âDonât share anything you wouldnât want publicly broadcasted,ââ he advises.
Conclusion
Generative AI offers immense potential but also comes with significant privacy and security challenges.⢠By âŁadopting best practices and treating AI tools with⤠caution, âbusinesses and individualsâ can mitigate these risks effectively.
6 Comments
AI as a coworker? Sounds like the start of a sci-fi movie plot!
Is relying on AI like setting the fox to guard the henhouse?
Gossamerb: AI, the coworker that never takes a coffee break!
Can AI steal your job before it even borrows your stapler?
Think AI is going to replace your water cooler chats?
Sapphirea: Rather trust a robot than my last colleague, honestly!