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AI Risks and Prevention in Business: Guarding against Potential Pitfalls

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July 12, 2023Hacker NewsDNS Filtering / Network Security

Security risk

Artificial intelligence (AI) has great potential to optimize internal processes in business. However, that also comes with legitimate concerns about unauthorized use, including risks of data loss and legal consequences. In this article, we will explore the risks associated with implementing AI and discuss steps to minimize the damage. In addition, we will examine regulatory initiatives by countries and ethical frameworks adopted by companies to regulate AI.

Security risk

AI phishing attacks

Cybercriminals can leverage AI in a number of ways to enhance their phishing attacks and increase their chances of success. Here are some ways AI can be exploited for phishing:

  • Automated Phishing Campaigns: AI-powered tools can automate the creation and deployment of phishing emails at scale. These tools can generate convincing email content, create personalized messages, and mimic a specific individual’s writing style, making phishing attempts appear more legitimate.
  • Spear Phishing with Social Engineering: AI can analyze large amounts of publicly available data from social media, professional networks, or other sources to gather information about potential targets. This information can then be used to personalize phishing emails, making them highly customized and difficult to distinguish from genuine communications.
  • Natural Language Processing Attack (NLP): AI-powered NLP algorithms can analyze and understand text, enabling cybercriminals to create phishing emails that are contextually relevant and more difficult for traditional email filters to detect. These sophisticated attacks can bypass security measures designed to identify phishing attempts.

To mitigate the risks associated with AI-enhanced phishing attacks, organizations must adopt strong security measures. This includes employees training to recognize phishing attempts, implement multi-factor authentication, and leverage AI-based solutions to detect and defend against evolving phishing techniques. hiring DNS filtering as the first layer of protection can further enhance security.

Security risk

Regulatory and legal risks

With the rapid development of AI, laws and regulations related to technology are constantly evolving. Regulatory and legal risks related to AI refer to the potential liability and legal consequences that businesses may face when implementing AI technology.

– As AI becomes more common, governments and regulators are starting to create laws and regulations governing the use of the technology. Failure to comply with these laws and regulations may result in legal and financial penalties.

– Liability for losses caused by AI systems: Businesses can be held liable for losses caused by their AI systems. For example, if an AI system makes a mistake that results in financial loss or loss to someone, that business can be held liable.

– Intellectual property disputes: Businesses may also face legal disputes regarding intellectual property when developing and using AI systems. For example, disputes may arise over ownership of the data used to train an AI system or over ownership of the AI ​​system itself.

Countries and Companies That Restrict AI

Regulatory Actions:

Several countries are implementing or proposing regulations to address AI risks, which aim to protect privacy, ensure algorithm transparency, and define ethical guidelines.

Example: The European Union’s General Data Protection Regulation (GDPR) establishes principles for responsible data use of AI systems, while being proposed AI Act strives to provide comprehensive rules for AI applications.

China has released AI-specific regulations, with a focus on data security and algorithmic accountability, while the United States is engaged in ongoing discussions about AI governance.

Corporate Initiatives:

Companies are taking proactive action to regulate the responsible and ethical use of AI, often through self-imposed restrictions and ethical frameworks.

Example: Google’s AI principles emphasize avoidance of bias, transparency, and accountability. Microsoft created the AI ​​and Ethics in Engineering and Research Committee (AETHER) to guide responsible AI development. IBM developed the AI ​​Fairness 360 toolkit to address bias and fairness in AI models.

Conclusion.

We strongly recommend implementing a comprehensive protection system and consulting the legal department regarding the associated risks when using AI. If the risks of using AI outweigh the benefits and your company’s compliance guidelines advise against using certain AI services in your workflow, you can block them using DNS filtering service from SafeDNS. By doing so, you can reduce the risk of data loss, maintain legal compliance, and comply with internal company requirements.

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