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ChatGPT and Cybersecurity: What does it offer for future of small businesses

Technology for MSMEs: Implementing comprehensive data security measures may be challenging, particularly for small and medium-sized organisations (SMEs) without the essential skills and resources.

chatgpt, ai, artificial intelligence, cyber security, cyber attacks, open ai, data privacy, data security, data breach
The potential of generative artificial intelligence is harnessed by OpenAI's ChatGPT, which promises to alter how humans interact with computers and automate tasks.

By Chris Connell

Technology for MSMEs: Artificial intelligence (AI) is becoming more common in today’s IT world and will gain traction in the next few years. Open AI unveiled their project ChatGPT (Chat Generative Pre-Trained Transformer) in November 2022, an AI chatbot that can rapidly answer basic and difficult inquiries. It has transformed the way jobs are accomplished in a variety of businesses. Despite its advantages, unscrupulous actors utilise it to spread viruses, disguising them from security measures and making them harder to detect.

The potential of generative artificial intelligence is harnessed by OpenAI’s ChatGPT, which promises to alter how humans interact with computers and automate tasks. One of ChatGPT’s most remarkable qualities is its ability to communicate like an actual human. It will respond to every enquiry or command with a human-like response. Many in the security community are concerned about whether the technology’s critical business data intake puts enterprises in danger of cyberattacks.

Protecting corporate data has become increasingly important in the digital era. Businesses must secure their sensitive information proactively in the face of rising cyber threats and data breaches. On the other hand, implementing comprehensive data security measures may be challenging, particularly for small and medium-sized organisations (SMEs) without the essential skills and resources.

Also read: AI automated customer contact centers: Building cyber-secure fortresses for MSMEs

Understanding What is ChatGPT

ChatGPT is an OpenAI AI language model that can talk with people in natural language. It employs a neural network design based on transformers to answer questions and assertions coherently. ChatGPT is trained on a large corpus of text data, allowing it to comprehend and reply to various topics.

Chatbots like ChatGPT can automate boring jobs or improve complicated business interactions by producing email sales campaigns, correcting computer code, or improving customer service.

Increased Social Engineering attacks with ChatGPT

Fake help requests, and even scripting using ChatGTP are all possibilities. The internet is brimming with materials to promote effective social engineering initiatives. Threat actors are advancing social engineering assaults by integrating several attack vectors, such as ChatGPT and other social engineering tactics.

ChatGPT can assist attackers in better creating a bogus identity, increasing the likelihood of their assaults succeeding.

ChatGPT Security Risks

One of the most serious commercial issues is that ChatGPT can go too far, creating elegant text with natural language responses that have little substance of value or, worse, inaccurate statements.

A chatbot might expose private and personally identifiable information (PII). Therefore, businesses must be careful about what data is sent into the chatbot and avoid disclosing confidential information. Collaboration with vendors with strict data usage and ownership rules is also essential.

Besides sensitive data provided by common users, businesses should be careful of prompt injection attacks, which may disclose earlier instructions provided by developers while configuring the tool or cause it to reject previously programmed commands.

Controlling data submitted to ChatGPT

ChatGPT is transitioning from hype to reality, and organisations are experimenting with practical deployment throughout their company to complement their other ML/AI-based solutions, but some caution is required, particularly when it comes to exchanging personal information.

Ultimately, the company is responsible for ensuring its users understand what information should and should not be shared with ChatGPT. Organisations should exercise extreme caution while submitting data in prompts. You should ensure that people who wish to experiment with LLMs may do so in a way that does not jeopardise organisational data.

Need for awareness about potential danger of chatbots

Organisations should carefully consider how they may use these new technologies to improve their operations. Don’t avoid these services out of fear and uncertainty, but instead devote some employees to investigating new tools that show promise so you can understand the dangers early and ensure proper safeguards are in place when early end-user adopters want to begin utilising the tools.

Also read: ‘Banks must embrace AI, ML, NLP to lend to SMEs that don’t have credit history, are untapped by formal banking’

Organisations should create policies on their secure web gateways (SWG) to detect the usage of AI tools. They can also apply data loss prevention (DLP) policies to identify what data is being transmitted to these tools for enhanced visibility.

Organisations should update their information security rules to ensure that the types of apps that are appropriate handlers of private data are adequately specified.

ChatGPT is a game changer since it provides a simple and powerful tool for AI-generated interactions. While there are various possible applications, businesses should know how attackers might utilise this technology to better their methods and the added dangers it may represent to their organisation.

ChatGPT here to stay

ChatGPT is a potent language model with the potential to transform natural language processing workloads. But, like with any technology, knowing the possible hazards while utilising ChatGPT in an application is critical. Data privacy and security, model performance, model bias, legal and regulatory compliance, and reliance on third-party services are all examples. To guarantee easy integration and minimise risks, thoroughly examine the model and the provider before integrating them into your application, and continually monitor and test the model’s performance and output.

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Chris Connell is Manager Director APAC at Kaspersky. Views expressed are the author’s own.

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First published on: 22-04-2023 at 14:33 IST