How Gen AI could be your biggest security risk

As the usage of GenAI systems spreads across the various sectors, multiple vulnerabilities are created for cybercriminals to exploit. In cases where techniques such as prompt injection are used by attackers to manipulate AI responses (or user sensitive information obtained by attacks) in enterprise applications including customer service chat bots, fraud detection systems or code generators the security of the organisation is at risk. The high value of the data processed by GenAI systems, such as proprietary models and customer information, is the target.

What are the risks associated to GenAI?

Prompt injection is a technique in which attackers make use of malicious inputs to deliberately manipulate the GenAI models and thereby influence them to behave in unwanted manners or reveal information which should otherwise be untouched. These are deceptive prompts that can fool AI systems to expose other people’s confidential data or perform actions that it was not meant or designed to do. 

In brief, prompt injection could lead to an AI assistant leaking personal details or issuing harmful commands . Such a malicious prompt could trick the AI to divulge confidential information or make it do certain things that it was not intended to. For the most part, these AI systems need massive data sets for training, which could contain sensitive or proprietary data. This data can go exposed or misused if not properly secured. Furthermore, user inputs given to GenAI tools might inadvertently leak confidential information if the system’s outputs are shared or stored insecurely.

Most GenAI tools are configured to run on complex infrastructures such as cloud services and APIs. If these components are not properly secured then they become an entry point for attackers. For instance, vulnerability in cloud configuration or API endpoint can be leveraged to abuse the access of unauthorised gains to AI systems. GenAI can be used by cybercriminals for writing convincing phishing emails. There is so much news about deepfake videos and malware code. GenAI is the clear reason for such crimes. The National Cyber Security Centre in the UK has warned that AI generated content can make scam emails more believable and thus more likely to succeed in a phishing attack .

Implications for Cybersecurity

As the GenAI is integrated into various sectors, severe cybersecurity implications are introduced. The scope for cyberattacks on those tools then grows at an ever more rapid pace as organisations increasingly adopt GenAI tools. In addition, GenAI technologies are developing at a rapid pace, faster than the existing regulatory frameworks, and it is difficult for organisations to be compliant with data protection laws and industry standards. The result of this regulatory lag is that vulnerabilities can be formed in data handling or its privacy practices. Thus, organisations need to ensure that they take proactive steps to tackle these challenges and strengthen their cybersecurity strategy with new regulations and keep themselves updated with evolving regulations to offset the risks involved with the use of GenAI.

To address these risks, organisations should consider the following strategies:

In order to reduce cybersecurity risks posed by GenAI  for organisations, it is imperative to adopt an integrated approach that includes securing the processes of its development, protection of data, ensuring the infrastructure security, educating a user as well as compliance with regulations.

First of all, secure development practices need to be implemented. Security assessment would allow finding and fixing vulnerabilities in AI models. Protecting data is essential. An organisation should make sure that training data is anonymised and stored securely. It has to be assured that there are strict access controls put in place to exclude unauthorised access to sensitive information. Maintaining infrastructure security is vital. Infrastructure which supports GenAI tools must be audited and updated on a regular basis. 

Secure configuration of the cloud services and monitoring of API usage can help to detect abnormal activities that may be a sign of a security breach. 

It is important to educate users about potential threats related to GenAI. The training should be on recognising phishing attempts and suspicious AI generated content. A high level of awareness among users can prevent a lot of security breaches. GenAI implementations must be compliant with relevant laws and industry standards by organisations. By making balance assignments regularly, risk assessment and cybersecurity policies can be updated and new threats countered.

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