Secure cloud fabric: Enhancing data management and AI development for the federal government

As such, the model itself must be recognized as a critical asset and protected, and the storage and computing systems on which the model is stored and executed must similarly be treated with high levels of security. Determining the ease of attacking a particular system will be an integral part of these AI suitability tests. The degree of vulnerability can be determined by characteristics such as public availability of datasets, the ability to easily construct similar datasets, and other technical characteristics that would make an attack easier to execute. One example of an application that could be particularly vulnerable to attack is a military system that automatically classifies an adversary’s aircrafts. The dataset for this task would likely consist of collected radar signatures of the adversary’s aircraft. This would therefore allow the adversary to craft an attack without ever having to compromise the original dataset or model.

• These machines would be able to understand an individual’s motives, reasoning, and needs to respond with personalized results.
• Each shall do so in consultation with the heads of other relevant agencies as the Secretary of Defense and the Secretary of Homeland Security may deem appropriate.
• By staying informed about relevant policies and taking proactive measures like regularly reviewing permissions granted for accessing personal information or using encryption tools when transmitting sensitive data online users can take control over their digital footprint.
• For example, for a social network that has seen itself mobilized to spread extremist content, it can be expected that input attacks aimed at deceiving its content filters are likely.
• They argue that the United States is currently the world’s leader in AI innovation, and strict regulations would severely hinder that.

With the volume of personal information available and leveraged to train AI systems, we need to consider the potential risks and implications of these technologies. Google has a long history of driving responsible AI and cybersecurity development, and we have been mapping security best practices to new AI innovation for many years. Our Secure AI Framework is distilled from the body of experience and best practices we’ve developed and implemented, and reflects Google’s approach to building ML and gen AI powered apps, with responsive, sustainable, and scalable protections for security and privacy. We will continue to evolve and build SAIF to address new risks, changing landscapes, and advancements in AI.

Enforcement

Government regulations that accelerate adoption of open source AI promises numerous benefits, including greater transparency, trust, and public oversight of algorithms. But challenges remain around privacy, security and sustainable maintenance of open source AI projects. Regulation is essential for managing the complex ethical and safety challenges of AI, yet it’s equally critical to promote a regulatory environment that spurs innovation and upholds the democratic nature of AI development. The executive order’s ambition is commendable and generally well-directed, but it still falls short in ways that may benefit incumbents disproportionately.

We now turn our attention to understanding the technical basis of these attacks in order to answer these questions. General George Patton may have won the D-Day campaign for the Allies without ever firing a shot. In support of the future D-Day landings, Patton was given charge of the First United States Army Group (FUSAG). To convince the German command that the invasion point would be Pas de Calais rather than Normandy, the FUSAG orchestrated a major force deployment—including hundreds of tanks and other vehicles—directly across the English Channel from it. “Microsoft allows customers who meet additional Limited access eligibility criteria and attest to specific use cases to apply to modify the Azure OpenAI content management features,” Microsoft explained. Earlier this year it was revealed that a government Azure server had exposed more than a terabyte of sensitive military documents to the public internet – a problem for which the DoD and Microsoft blamed each other.

Domino for Government

While AI is not new, and the government has been working with AI for quite some time, there are still challenges with AI literacy and the fear of what risks AI can bring to organizations. The problem was that when ChatGPT came out and captured the world’s attention, there was widespread sensationalism in this “new” technology. If your organization is taking generative AI models from any prominent provider of a large language model, it’s critical to prepare mitigation layers to reduce the risks and deliver the value you expect from this rapidly evolving technology. There’s a real risk in AI – particularly generative AI – of exacerbating inequities, which sometimes comes from the training data. For example, during the pandemic, minorities were more devastated by COVID-19 than wealthy or broader communities.

Read more about Secure and Compliant AI for Governments here.