The risks and preventions of artificial intelligence in the business world: protection from potential pitfalls

July 12, 2023The news about hackersDNS Filtering / Network Security

Artificial intelligence (AI) has immense potential for optimizing internal processes within companies. However, it also comes with legitimate concerns about unauthorized use, including data loss risks and legal ramifications. In this article, we will explore the risks associated with implementing AI and discuss measures to minimize the harm. Furthermore, we will look at the regulatory initiatives of countries and the ethical frameworks adopted by companies to regulate AI.

Security risks

AI phishing attacks

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

• – Automated Phishing Campaigns: AI-powered tools can automate the creation and dissemination of phishing emails at scale. These tools can generate compelling email content, craft personalized messages, and mimic a specific individual’s writing style, making phishing attempts appear more legitimate.

• – Socially engineered spear phishing: AI can analyze large amounts of publicly available data from social media, professional networks, or other sources to glean information about potential targets. This information can then be used to personalize phishing emails, making them highly personalized and difficult to distinguish from authentic communications.

• Natural Language Processing (NLP) Attacks: AI-powered NLP algorithms can parse and understand text, allowing cybercriminals to create phishing emails that are contextually relevant and harder to detect than traditional email filters. These sophisticated attacks can bypass security measures designed to identify phishing attempts.

To mitigate the risks associated with AI-enhanced phishing attacks, organizations should have robust security measures in place. This includes training employees to recognize phishing attempts, implementing multi-factor authentication, and using AI-powered solutions to detect and defend against evolving phishing techniques. Employing DNS Filtering as the first layer of protection can further improve security.

Regulatory and legal risks

With the rapid development of AI, the laws and regulations related to the technology are still evolving. Regulatory and legal risks associated with AI refer to the potential liability and legal consequences companies may face when implementing AI technology.

– As AI becomes more prevalent, 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 damages caused by AI systems: Businesses can be held liable for damages caused by their AI systems. For example, if an AI system makes a mistake that results in financial loss or harm to an individual, the business can be held liable.

– Intellectual property disputes: Businesses can also face legal disputes related to intellectual property during the development and use of artificial intelligence systems. For example, disputes can arise over ownership of the data used to train AI systems or over ownership of the AI ​​system itself.

Countries and companies restricting AI

Regulatory measures:

Several countries are implementing or proposing regulations to address the risks of AI, with the aim of protecting privacy, ensuring algorithmic transparency and setting ethical guidelines.

Examples: The European Union’s General Data Protection Regulation (GDPR) sets out principles for the responsible use of data by AI systems, while the proposed AI Law seeks to provide comprehensive rules for AI applications .

China has released AI-specific regulations focusing on data security and algorithmic accountability, while the US is engaged in ongoing discussions on AI governance.

Company initiatives:

Many companies are taking proactive steps to govern the responsible and ethical use of AI, often through self-imposed restrictions and ethics frameworks.

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

Conclusion.

We strongly recommend that you implement comprehensive safeguards and consult with 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 a DNS filtering service from SafeDNS. This way, you can mitigate data loss risks, maintain legal compliance, and adhere to internal business requirements.