It’s no secret. We’re in the midst of an Artificial Intelligence (AI) boom!
- Technical innovations in AI are occurring at a rate we’ve rarely seen.
- Companies are embracing and rapidly deploying newly developed AI solutions to automate business processes, improve customer experiences, and drive decision-making.
- Individuals are interacting directly with AI systems in hopes of offloading routine tasks to machines so that they may focus on higher-order responsibilities.
But, lurking in the shadows of this AI revolution is the specter of increased security risk.
Observations akin to those above were made recently by leaders in industry, academia, and government during the first international AI Safety Summit. These leaders recognized the vast transformative potential of AI for individuals and societies, but they also acknowledged that AI poses significant risks. The challenge they issued to the international community was the urgent need to establish guidelines for responsible AI development.
In direct response to the summit, the US Cybersecurity Infrastructure and Security Agency and the UK National Cyber Security Center jointly published Guidelines for Secure AI System Development to help providers of AI systems make informed decisions regarding their design, development, deployment, and operations. While advocating the use of Secure-by-Design principles to mitigate standard cybersecurity threats, the guidelines also introduce risks and threats that result from novel security vulnerabilities specific to AI systems.
In the cybersecurity arms race, attackers have traditionally had a significant advantage over defenders. Attackers only have to find one weakness to succeed, the defenders have to fix them all. The security vulnerabilities of AI systems tilt the advantage even more in the direction of threat actors.
As a simple example, consider the vulnerability of a deployed model. Attackers have successfully demonstrated Evasion Attacks and Prompt Injection Attacks that can be used to significantly degrade or alter the intended performance of a model, reveal sensitive data to unintended parties, and reconstruct the data upon which a model was trained. To counter these attacks, AI developers must protect the model continuously, and ensure that attackers are unable tamper with the model, data, or prompts. With the increased interaction by users with AI systems, the added twist for defenders is that a legitimate user may unwittingly and unintentionally mimic a “threat actor.” So, not only must developers implement defenses that identify and prevent malicious activity from intentional attackers, they must also establish and implement guardrails for users that limit the functionality of the model to comport with its intended scope.
At SineWave, we’re focused on investment aimed at accelerating digital transformation. We believe the digital ecosystem can make work and life easier, and more secure. SineWave is positioned to evaluate AI solutions that align with AI Safety guidelines to make AI systems safe, resilient, fair, and reliable for everyone.
