By Abhishek R Srinivasan, Director – Product Management, Array Networks
Generative AI has seen a sharp rise in adoption in the last year. While the technology promises innovation and productivity, risks about data security and breaches plague organisations. Some of the risks associated with Generative AI are data misuse, data breaches, and data poisoning. This is because Generative AI is a Large Language Model (LLM) that relies on a significant amount of data to produce outputs. With AI’s growing adoption and development, understanding and mitigating inherent risks becomes increasingly important for organisations.
Top three Gen AI-associated risks
Most Generative AI risks stem from how users suggest prompts and how the tool collects, stores, and processes information. Here are three key risks associated with Generative AI tools.
Risk of data leak
Generative AI systems learn and improve through large datasets (gained from the internet or provided by an organisation) and user-provided prompts. Prompts are used for immediate analysis and are often retained within the AI’s backend for future training and model optimisation. They are also often analysed by human operators. This introduces a potential risk of inadvertently exposing sensitive information.
Employees interacting with such AI tools may unknowingly disclose confidential or sensitive details within their prompts. For example, a recent incident revealed that Samsung employees uploaded confidential code to ChatGPT, exposing the company’s most sensitive information. They also used ChatGPT to create meeting notes and summarise business reports, inadvertently exposing confidential information.
While this is only one example, there are many instances where employees unknowingly uploaded sensitive information to the AI software, putting the enterprise’s data at risk.
Vulnerability in AI tools
Generative AI tools, like any software, are not immune to security vulnerabilities. These vulnerabilities can pose significant risks to user data and broader system security. A few of the potential breaches include:
- Data breaches: AI systems store a large dataset of information, which could be at risk if hackers exploit a vulnerability and infiltrate the computer systems or network hosting the Generative AI tool. This way, the hackers could access sensitive data, including user-generated prompts, internal company documents, and more.
- Model manipulation: Malicious actors could manipulate the AI model, potentially leading to biased or inaccurate outputs, misinformation campaigns, or harmful content.
Data poisoning or stealing
The dataset that Generative AI models rely heavily on can often be scraped from the internet, making the data vulnerable to data poisoning and stealing.
Data poisoning is where malicious threat actors inject misleading or erroneous data into the existing training sets, corrupting or manipulating the AI’s learning process. This could lead to biased, inaccurate, or even harmful user outputs.
Data stealing can happen when an AI organisation lacks adequate data storage and security measures, potentially exposing sensitive user information or proprietary intellectual property. This stolen data could be used for various malicious purposes, such as identity theft.
Another adversity that arises from the use of Generative AI is hackers leveraging the technology to conduct threats. Hackers use Generative AI to launch highly sophisticated phishing attacks where users cannot differentiate between legitimate and hacker-written emails, leading to cyberattacks. Threat actors also leverage AI for deep fake attacks, using a legitimate authority’s facial expressions, voice, or audio to manipulate the target to take certain actions or divulge sensitive information.
How to mitigate Generative AI risks?
Here are a few ways organisations can mitigate Generative AI risks and safely leverage this technology.
- Educate employees: The most crucial step is educating employees on the potential risks of using an AI tool. Education on collecting, processing, and storing information can enable them to conduct prompts correctly. Organisations could prevent data breaches and other related attacks by creating guidelines on the usage of AI and mentioning compliance or regulatory guidelines required in that industry.
- Conduct a legal review: Every AI tool has its regulations. These need to be reviewed by the organisation’s legal department to ensure they align. It is even more important in the case of highly regulated industries such as financial services or healthcare. The legal team can warn the company of data collection, storage, and usage terms and conditions and see whether or not they align with the firm.
- Protect data and data sets for training AI: Data security should be the top priority for organisations building their own Generative AI tools. They must prioritise data that aligns with the intended purpose and avoid introducing biases or sensitive information for data selection. Anonymising information is also crucial as it minimises the risk of identifying individuals while preserving data utility for the AI model. Organisations should also prioritise data security measures by establishing clear data governance policies and access control mechanisms to restrict access to sensitive data only to authorised personnel.
- Set up zero-trust systems: Organisations can grant access to sensitive data and information only to the specific employees requiring it for their tasks by employing zero-trust security models. This granular control significantly reduces the overall attack surface and prevents scenarios where disgruntled employees or accidental mistakes could lead to data breaches or misappropriation usage while operating AI tools.
Conclusion
Generative AI has the potential to revolutionise industries with its ability to automate tasks, generate unique content, and personalise user experiences. However, it also comes with inherent risks. These include data privacy concerns, bias, and the possibility of misuse.
Instead of preventing the use of Generative AI, combating risks with preventive measures will enable organisations to leverage this technology fully. These risks can be mitigated by implementing robust security measures, fostering responsible data governance practices, and prioritising user education.
