The impact of the AI wave on enterprise security

In today's digital world,AIThe rapid development of (AI) technology is leading the innovation and development of enterprises. In particular, the wave of generative AI products, such as natural language generation (NLG) systems, image synthesis, and video synthesis, is changing the business operations and user experience of enterprises. However, this wave also brings some security risks and challenges. We will explore the impact of the wave of generative AI products on enterprise security and provide corresponding response strategies.

1. The threat of cyber attacks intensifies

AI has been reducedhackerbarriers to entry. Hackers can use generative AI to quickly integrate various network attack methods, conveniently "weaponize" attack methods, and potentially achieve innovative attack methods. PricewaterhouseCooperscyber securityThe team has clearly observed that social engineering attacks such as phishing emails received by customers have increased significantly in recent months, coinciding with the widespread use of ChatGPT; ChatGPT has also been found to be used to batch generate more confusing phishing websites.

2. Leakage of sensitive enterprise data

security expertWe are concerned about the possible leakage of corporate sensitive data. Improper input behavior by employees may cause sensitive data to be retained in the database of generative AI products. OpenAI’s privacy policy shows that the content entered by users when using ChatGPT will be used to train its AI algorithm model. Furthermore, ChatGPT has been exposed to serious security issues. Due to vulnerabilities in the open source library, some users were able to see the titles of other users' conversation history. At present, many technology giants such as Amazon and Microsoft have reminded employees not to share sensitive data with ChatGPT.

3. AI poisoning risk

Training data and poisoning the model are common security threats faced by AI. Malicious data will have a negative impact on the results of the AI algorithm. If operations management relies heavily on AI, wrong decisions may be made on key issues. On the other hand, generative AI also has potential "bias" issues. Similar to the "100 Bottles of Poison for AI" campaign participated by many well-known experts and scholars, companies should proactively and strategically respond to the threat of AI poisoning when developing or using AI.

4. Privacy protection issues

The AI pre-training phase requires a large amount of data collection and mining, which may include a lot of private information of customers and employees. If AI cannot properly protect and anonymize this private information, it may lead to privacy leaks, and this private information may even be abused to analyze and speculate on user behavior. For example, the mobile application market is flooded with image generation software. Users only need to upload multiple avatars of themselves, and the software can generate composite photos of different scenes and themes. However, how software companies use the avatars uploaded by these users and whether it will bring about privacy protection and other security risks are worthy of attention and response.

5. Enterprise security compliance risks

In the absence of effective management measures, the mass adoption of AI products may lead to security compliance issues, which is undoubtedly a huge challenge for enterprise security managers. It was reviewed and approved by the Cyberspace Administration of China and approved by six departments including the National Development and Reform Commission and the Ministry of Education.generative artificial intelligenceThe Interim Measures for Service Management have been officially implemented on August 15, 202312. They put forward basic requirements from the perspectives of technology development and governance, service specifications, supervision and inspection, and legal responsibilities, and established basic compliance for the adoption of generative AI. regulatory framework.

AI security threat scenarios

After understanding the security risks introduced by generative AI, the following will analyze how problems arise in more specific security threat scenarios, and explore the subtle impact generative AI has on enterprise security.

1. Social engineering attacks

World-renowned hacker Kevin Mitnick once said: "The weakest link in the security chain is the people factor." A common tactic of social engineering hackers is to use sweet words to deceive corporate employees, and the emergence of generative AI has greatly facilitated social engineering attacks. Generative AI can generate highly realistic fake content, including fake news, fake social media posts, fraudulent emails, etc. These fake content may mislead users, spread false information, or trick corporate employees into making wrong decisions. Generative AI can even be used to synthesize sounds or videos to make them appear real, which could be used to commit fraud or falsify evidence. The Telecommunications Cybercrime Investigation Bureau of Baotou City Public Security Bureau announced a case of telecom fraud using intelligent AI technology. Criminals used AI face-changing technology to defraud 4.3 million yuan in 10 minutes.

2. Unintentional violations by employees

Many technology manufacturers have begun to actively lay out the generative AI track and integrate a large number of generative AI functions into products and services. Employees may have inadvertently used generative AI products without carefully reading the user terms of use before using them. When enterprise employees use generative AI, they may input content containing sensitive information, such as financial data, project materials, company secrets, etc., which may lead to the leakage of sensitive enterprise information. To prevent generative AI from leaking sensitive corporate information, companies need to take comprehensive security measures: including enhancing data leakage protection technology and restricting employees’ online behavior; at the same time, they need to conduct security training for employees to improveData Securityand confidentiality vigilance, etc. Once an employee's violation is discovered, the company needs to immediately assess the impact and take timely action.

3. Inevitable discrimination and prejudice

The reason why AI may have discrimination and bias is mainly due to the characteristics of its training data and model design. Training data from the Internet reflects real-world biases, including race, gender, culture, religion, and social status. During the processing of training data, there may not be adequate screening and cleaning measures to exclude biased data. Likewise, insufficient attention may be paid to reducing bias in model design and algorithm selection for generative AI. Algorithmic models capture biases in the training data during the learning process, causing the generated text to be similarly biased. While eliminating bias and bias in generative AI is a complex challenge, there are steps companies can take to help mitigate it3.

4. Compromise on privacy protection

In the process of using AI products, in order to pursue efficient automation and personalized services, companies and individuals may make some compromises in privacy protection and allow AI to collect some private data. In addition to users disclosing personal privacy content to AI during use, AI may also analyze user input and use algorithms to infer the user's personal information, preferences or behavior, further infringing on the user's privacy. Data desensitization and anonymization are common privacy protection measures, but they may lead to the loss of part of the data information, thereby reducing the accuracy of the generated model. A balance between personal privacy protection and the quality of generated content needs to be found. As an AI provider, you should provide users with a transparent privacy policy statement to inform them of the collection, use and sharing of data, so that users can make informed decisions.

5. Megatrends in regulatory compliance

At present, the legal compliance risks faced by AI mainly come from aspects such as "illegal content" and "intellectual property infringement". In the absence of supervision, AI may generate illegal or inappropriate content, which may involve insults, slander, pornography, violence and other illegal or illegal elements; on the other hand, generative AI may be based on existing copyrighted content. protected content, which may result in intellectual property infringement. Enterprises using generative AI must conduct compliance reviews to ensure that their applications comply with relevant regulations and standards and avoid unnecessary legal risks. Enterprises should first evaluate whether the products they use comply with the provisions of the "Interim Measures for the Management of Generative Artificial Intelligence Services". At the same time, they need to pay close attention to updates and changes in relevant regulations and make timely adjustments to ensure compliance. When companies use generative AI with suppliers or partners, they need to clarify the rights and responsibilities of each party and stipulate corresponding obligations and restrictions in the contract.

How to deal with the risks and challenges of AI

Users or corporate employees need to realize that while enjoying the various conveniences brought by AI, they still need to strengthen the protection of sensitive information such as their personal privacy.

1. Avoid leakage of personal privacy

Before using AI products, employees should ensure that the service provider will reasonably protect the privacy and security of users, carefully read the privacy policy and user terms, and choose a reliable provider that has been verified by the public as much as possible. Try to avoid entering personal privacy data during use. Use virtual identities or anonymous information in scenarios where real identity information is not required. Any possible sensitive data needs to be fuzzified before input. On the Internet, especially social media and public forums, employees should avoid excessive sharing of personal information, such as names, addresses, phone numbers, etc., and not easily expose information to publicly accessible websites and content.

2. Avoid misleading generated content

Due to the limitations of AI's technical principles, its results are inevitably misleading or biased. Industry experts are also constantly studying how to avoid the risk of data poisoning. For important information, employees should verify it from multiple independent and credible sources. If the same information appears in only one place, more investigation may be needed to confirm its authenticity. Look for the information stated in the results to be supported by solid evidence. If it lacks substantial basis, you may need to treat the information with skepticism. Identifying AI’s misinformation and bias requires users to maintain critical thinking, continuously improve their digital literacy, and understand how to use its products and services safely.

Compared with the open attitude of individual users, enterprises are still waiting to see AI. The introduction of AI is both an opportunity and a challenge for enterprises. Enterprises need to consider overall risks and make some response strategic arrangements in advance. The suggestions are as follows:

1. Enterprisecyber securityAssess or improve defense capabilities

The primary challenge facing enterprises remains how to defend against next-generation cyberattacks brought about by AI. It is imperative for enterprises to assess the current network security status, clarify whether the enterprise has sufficient security detection and defense capabilities to deal with these attacks, identify potential network security defense vulnerabilities, and take corresponding reinforcement measures to actively respond. In order to achieve the above goals, it is recommended that enterprises conduct offensive and defensive confrontation drills based on these real network attack threat scenarios, that is, network security "red and blue confrontation." Discover possible network security defense shortcomings in advance from different attack scenarios, and comprehensively and systematically repair defense flaws to protect the enterprise's IT assets and data security.

2. Deploy an internal AI testing environment within the enterprise

If you want to understand the technical principles of AI and better control the results generated by the AI model, companies can consider establishing their own AI sandbox testing environment internally to prevent uncontrollable generative AI products from affecting corporate data. potential threat. By testing in an isolated environment, companies can ensure that accurate and unbiased data can be used for AI development, and can more confidently explore and evaluate model performance without worrying about the risk of sensitive data leakage. An isolated testing environment can also avoid data poisoning and other external attacks on AI, maintaining the stability of the AI model.

3. Establish a risk management strategy for AI

Enterprises should incorporate AI into the target scope of risk management as early as possible, and supplement and modify risk management frameworks and strategies accordingly. Conduct risk assessments on business scenarios using AI, identify potential risks and security vulnerabilities, formulate corresponding risk plans, and clarify response measures and responsibility allocation. Establish a strict access management system to ensure that only authorized personnel can access and use AI products approved by the enterprise. At the same time, users’ usage behavior should be regulated, and corporate employees should be trained on AI risk management to enhance employees’ security awareness and response capabilities. Entrepreneurs should also adopt a privacy by design approach when developing AI applications to make it clear to end users how the data they provide will be used and what data will be retained.

4. Form a dedicated AI research working group

Enterprises can assemble professional knowledge and skills within the organization to jointly explore the potential opportunities and risks of AI technology, and invite members who understand related fields to participate in the working group, including data governance experts, AI model experts, business domain experts, legal compliance experts, etc. Enterprise management should ensure that working group members have access to the data and resources they need to explore and experiment, while encouraging working group members to experiment and validate in test environments to better understand the potential opportunities and business applications of AI. Scenarios to obtain the benefits of applying advanced technology while balancing the risks.

Conclusion

The development and application of AI are having a major impact on technology, which may trigger a new productivity revolution. AI is a powerful technology that combines advances in deep learning, natural language processing, and big data to enable computer systems to generate content in human language. Enterprises and employees should control this powerful technology and ensure that its development and application are carried out within the framework of legal, ethical and social responsibility. This will be an important issue in the future.