The future of risk is already here! Prepare for GenAI risks and compliance

For decades, the application of Machine Learning and Artificial Intelligence has been steadily increasing. However, the emergence of GenAI was a turning point moment in the democratization of AI across all domains. This innovation has introduced entirely new workflows, processes, and products, capable of providing a competitive edge to any application or business that embraces it.

As a result, AI is becoming omnipotent, making it essential to understand potential threats and vulnerabilities in these systems to safeguard deployments and integrations. While business considerations are often prioritized, the importance of security, along with compliance with data protection laws and regulations, is increasingly critical. This is especially true as organizations adopt more complex models and integrations potentially handling sensitive data and use cases.

A proactive solution

Existing security solutions for GenAI primarily focus on filtering malicious inputs and outputs. While this approach can mitigate certain risks, it remains fundamentally reactive. These approaches, while useful, are often insufficient and fall short of preemptively preventing attacks. We advocate for a more proactive strategy to secure models and integrations more effectively.

One of our key components to GenAI security is model introspection. It involves a deep examination of the inner workings of the foundational model. The aim is to assess the robustness of the model and the factors that most influence its decision-making processes. This knowledge helps better steer models' inputs and outputs and ensure robust and proactive performance assessment and overall security.

Complementing model introspection, our solution offers an advanced failure mode analysis. ​​This involves the continuous generation of tests that mimic potential attack scenarios, including rigorous vulnerability scanning and red teaming exercises that demonstrate system jailbreaking and its potential negative impacts. These meticulously designed activities probe models to gain valuable insights, proactively strengthening defense mechanisms. This continuous feedback loop is a key component of our solution, ensuring that the security posture of the models is consistently improving and evolving to counter both existing and emerging threats.

Compliance - The EU AI Act example

The huge potential of these systems requires well-thought-out frameworks and regulations to manage security, privacy, transparency, and integrity, and to build trust among users. Recognizing this imperative, the European Union enacted the Artificial Intelligence Act (EU AI Act) in March of this year. This legislation is regarded as the world's first comprehensive framework for AI, setting a global precedent for the responsible and ethical development and deployment of artificial intelligence technologies.

The Act prescribes rules for putting into service or using AI systems in the EU (or elsewhere if it affects users in the EU or their data). These include rules on data quality, transparency, human oversight, and accountability. The regulation's requirements and obligations are included in our solution for compliance checks (via actual scans, when applicable), providing a step-by-step path to ensuring adherence to the Act.

Systems and applications using GenAI will need such mechanisms and tools to detect, prevent, and mitigate adversarial threats, address bias, privacy concerns, or ethics issues, and ensure fairness and transparency in the systems being developed or integrated. Our solution provides a crucial toolset for implementing these requirements, managing risk, and ensuring compliance.