Author: Renyuan Dong, General Manager of JFag Greater China.
As AI applications continue to scale and large language models (LLMs) become commercialized, developers are increasingly tasked with packaging artificial intelligence (AI) and machine learning (ML) models with software updates or new software. While AI ML has a lot to offer in terms of innovation, it also heightens concerns because many developers don't have enough bandwidth to securely manage their development.
Security vulnerabilities can inadvertently introduce malicious ** into the AI ML model, giving threat actors the opportunity to lure developers into using OSS model variants to infiltrate corporate networks and cause further damage to the organization. There are even cases where developers are increasingly using generative AI to create, but don't know if their generation is compromised, which can also lead to long-term security threats. Therefore, it is important to conduct a proper review from the outset in order to proactively reduce the threat of damage to the software chain.
As threat actors find ways to exploit AI ML models, threats will continue to haunt security teams. As the number and scale of security threats continue to grow, in 2024 developers will place more emphasis on security and deploy the necessary safeguards to ensure the resiliency of their enterprises.
The evolving role of the developer
It's a relatively new practice for developers to have security in mind at the beginning of the software lifecycle. More often than not, binary-level security is considered to be just the "icing on the cake". Threat actors will take advantage of this oversight, looking for ways to model ML against the organization, finding ways to inject malicious logic into the final binary.
Similarly, many developers are unable to embed security in the initial stages of development because they do not have the necessary training. The main impact of this is that AI-generated and trained on open-source repositories are often not properly vetted for vulnerabilities and lack overall security controls to protect users and their organizations from exploitation. While this may save time and other resources in job functions, developers are unknowingly exposing their organizations to numerous risks. Once these exploits are implemented in the AI ML model, these exploits will have a more serious impact and may go undetected.
With the widespread use of AI, the traditional developer role is no longer sufficient to cope with the ever-changing security environment. As we move into 2024, developers must also become security professionals, reinforcing the idea that DevOps and DevSecOps can no longer be seen as separate job functions. By building a security solution from the start, developers can not only ensure maximum efficiency for critical workflows, but also increase confidence in the organization's security.
With "shift left", safeguards are installed from the start
If security teams are to remain vigilant against threats in the new year, the security of the ML model must continue to evolve. However, with the massive adoption of AI, teams can't identify the necessary security measures until later in the software lifecycle, because by then, it may be really too late.
The top management of the organization responsible for security must move "left" in software development. By sticking to this approach, you're able to secure all components of the software development lifecycle from the start and improve the security posture of your organization as a whole. When applied to AI ML, shift-left not only confirms whether the ** developed in the external AI ML system is secure, but also ensures that the AI ML model being developed is not malicious and meets the license requirements.
Looking ahead to 2024 and beyond, the threats surrounding AI and ML models will persist. If teams are to continuously defend against attacks from threat actors and protect their organizations and their customers, ensuring that security is built into from the beginning of the software lifecycle will be critical.
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