Managing AI Security Threats

Progress In AI, ML, and LLMs

In recent years, there has been significant progress in the development of large language models (LLMs) and other AI technologies. These models are trained on massive datasets of text and code and they can be used to perform a variety of tasks including natural language processing, machine translation, and code generation.

Some of the key players involved in the development of LLMs and other AI technologies include:

Potential challenges for cybersecurity organizations

The progress in LLMs and other AI technologies poses a number of potential challenges for cybersecurity and GRC organizations. LLMs, or large language models, have the potential to pose significant cybersecurity risks for organizations. These models are trained on vast amounts of data and can generate highly realistic text, including phishing emails, fraudulent messages, and misleading content. This can lead to a range of security threats, including social engineering attacks, identity theft, and data breaches.

For example, these models could be used to generate realistic-looking phishing emails or to create malware that is more difficult to detect and block. Hackers could use these deepfakes to impersonate executives or other trusted individuals, tricking employees into revealing sensitive information or transferring funds. Additionally, LLMs could be used to create convincing fake news articles or social media posts, which could be used to spread disinformation or manipulate public opinion.

In addition, LLMs could be used to automate a variety of tasks that are currently performed by human security analysts, such as threat hunting and vulnerability assessment. This could lead to a shortage of skilled security professionals, as well as a decrease in the effectiveness of traditional security measures.

Recommendations for cybersecurity organizations

To address the challenges posed by LLMs and other AI technologies, cybersecurity organizations should:

Invest in research and development of new security technologies that can defend against these threats.
Partner with other organizations, such as academic institutions and government agencies, to share information and best practices.
Develop and implement training programs for security professionals to help them stay up-to-date on the latest threats.
By taking these steps, cybersecurity organizations can help to ensure that they are prepared to defend against the evolving threat landscape.

Updating Risk Assessments For The World of AI & LLMs

It is crucial for security and risk assessments to include key questions that assess a company's security posture as it relates to the threats from LLMs and AI on cybersecurity & GRC teams. This is because these technologies have the potential to significantly impact an organization's security posture and the effectiveness of its GRC teams.

To assess an organization's security posture as it relates to LLMs and AI, some key questions that should be considered in security and risk assessments include:

What types of sensitive information does the organization collect, store, and transmit, and how could this information be targeted by LLMs and AI-based attacks?
How are employees trained to identify and respond to potential threats from LLMs and AI, such as phishing attacks or deepfakes?
What technical controls are in place to detect and prevent LLM and AI-based attacks, such as network monitoring and intrusion detection systems?
Are there policies and procedures in place for the use of LLMs and AI within the organization, including access controls and monitoring requirements?
Are external vendors and partners subject to the same security standards as internal teams and are they adequately vetted before granting them access to sensitive data?

By including these questions in security and risk assessments, organizations can gain a better understanding of the potential risks posed by LLMs and AI, as well as identify areas where additional controls may be needed to protect against these threats. It is important to consider these risks seriously, as LLMs and AI are increasingly being used by cybercriminals to carry out sophisticated attacks that can be hard to detect or prevent.