AI Regulation: Are Governments Up to the Task?

Oregon Establishes State Government AI Advisory Council Privacy Compliance & Data Security

Secure and Compliant AI for Governments

The methods underpinning the state-of-the-art artificial intelligence systems are systematically vulnerable to a new type of cybersecurity attack called an “artificial intelligence attack.” Using this attack, adversaries can manipulate these systems in order to alter their behavior to serve a malicious end goal. As artificial intelligence systems are further integrated into critical components of society, these artificial intelligence attacks represent an emerging and systematic vulnerability with the potential to have significant effects on the security of the country. GPAI is a voluntary, multi-stakeholder initiative launched in June 2020 for the advancement of AI in a manner consistent with democratic values and human rights. GPAI’s mandate is focused on project-oriented collaboration, which it supports through working groups looking at responsible AI, data governance, the future of work, and commercialization and innovation.

Secure and Compliant AI for Governments

AI can be trained to detect intrusion and malware, identify and respond to data breaches, predict user behavior, and stop phishing. Theory of mind is a type of AI that is still very much a theory, though data scientists are actively working toward it. These machines would be able to understand an individual’s motives, reasoning, and needs to respond with personalized results. The goal would be for these machines to learn more quickly and with fewer examples than limited memory machines. Domino’s Enterprise AI and MLOps Platform helps government agencies integrate AI into their missions rapidly, safely and cost-effectively.

Enabling Government Agencies To Scale Beyond Team-Level Agile Practices

He has extensive experience with traditional, relational analytics and text analytics solutions for various industries including compliance and governance. Originally from Northern Virginia, he recently relocated with his family from Washington, DC to Annapolis, MD. Even with the adoption of state-of-the-art deployment safeguards, robustly safe deployment is difficult to achieve and requires close attention and oversight. Further, developers can’t simply bolt on safety features after the fact; the model’s potential for harm must be considered at the development stage.

  • Moreover, purchasing a ready-made solution allows for quicker implementation and requires less expertise.
  • Furthermore, OMB has been further tasked with establishing systems to ensure agency compliance with guidance on AI technologies, including ensuring agency contracts for purchasing AI systems align with all legal and regulatory requirements and yearly cataloging of agency AI use cases.
  • In many applications, data is neither considered nor treated as confidential or classified, and may even be widely and openly shared.
  • Additionally, clients can optionally use IBM Cloud Satellite to automate and simplify deployment and day two operations of their VMware Private AI with OpenShift environments.
  • We want America to maintain our scientific and technological edge, because it’s critical to us thriving in the 21st century economy.
  • Continuing the analogy, poisoning attacks would be the equivalent of hypnotizing the German analysts to close their eyes anytime they were about to see any valuable information that could be used to hurt the Allies.

Because content filters are now being used as the first and, in many respects, only line of defense against terrorism, extremism, and political attack on the Internet, important parts of society would be left defenseless in the face of successful AI attacks. These attacks give adversaries free reign to employ these platforms with abandon, and leave these societal platforms unprotected when protection is needed more than ever. In poisoning attacks, the attacker seeks to damage the AI model itself so that once it is deployed, it is inherently flawed and can be easily controlled by the attacker. Unlike input attacks, model poisoning attacks take place while the model is being learned, fundamentally compromising the AI system itself. This policy will improve the security of the community, military, and economy in the face of AI attacks. But for policymakers and stakeholders alike, the first step towards realizing this security begins with understanding the problem, which we turn our attention to now.

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On the other side of the Atlantic, the United States has struggled with technology regulation, often falling behind the rapid pace of technological advancements. Congress’s track record in legislating technology is less than stellar, marked by delays, political polarization, and a general lack of expertise. This issue is exacerbated by the partisan nature of American politics, which often results in gridlock when attempting to pass meaningful tech-related legislation. As AI continues to advance, there’s a fine line for legislators to protect the people without stifling innovation. The jury is out on whether these guiding principles from the president will achieve this balance.

By leveraging this technology, government agencies can unlock the full potential of cloud-based resources, while still maintaining the security, privacy, and compliance requirements that are essential to their mission. Because of this, secure cloud fabric is likely to play an increasingly important role in the federal government’s digital transformation efforts in the years to come. (b)  Promoting responsible innovation, competition, and collaboration will allow the United States to lead in AI and unlock the technology’s potential to solve some of society’s most difficult challenges.

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The critical next steps in AI development should be built on the views of workers, labor unions, educators, and employers to support responsible uses of AI that improve workers’ lives, positively augment human work, and help all people safely enjoy the gains and opportunities from technological innovation. As enterprises prepare to accelerate innovation in the coming year, it will be critical to leverage the latest technologies including AI and hybrid cloud, while addressing evolving regulatory requirements. While regulations will likely continue to change, IBM remains committed to helping clients improve resiliency and performance, address their compliance challenges, enable security and manage risk. Many of the challenges ahead stem from the development and deployment of the most capable and generally capable models. Capabilities that would meet this standard may include significantly enabling the acquisition of weapons of mass destruction, exploiting vulnerabilities in safety-critical software systems, synthesizing persuasive disinformation at scale, or evading human control. Protecting against attacks that do not require intrusions will need to be based on profiling behavior that is indicative of formulating an attack.

What is the future of AI in security and defense?

With the capacity to analyze vast amounts of data in real-time, AI algorithms can pick up on anomalies and patterns the human eye could easily overlook. This swift detection enables organizations to neutralize threats before they escalate, making AI an invaluable tool in the arsenal of security experts.

In conclusion, balancing the benefits of AI with the need for robust data privacy and security makes the AI-driven world go round. (iv)   convening a cross-agency forum for ongoing collaboration between AI professionals to share best practices and improve retention. (i)    Within 120 days of the date of Director of NSF, in collaboration with the Secretary of Energy, shall fund the creation of a Research Coordination Network (RCN) dedicated to advancing privacy research and, in particular, the development, deployment, and scaling of PETs. The RCN shall serve to enable privacy researchers to share information, coordinate and collaborate in research, and develop standards for the privacy-research community.

Microsoft injects ChatGPT into ‘secure’ US government Azure cloud

This is already happening for many common AI tasks, including illicit content filters and computer vision tasks. Because a single organization specializes in building the AI system, it may be able to better invest resources to protect its system from attacks. However, the creation of “monocultures” in this setting amplify the damage of an attack, as a successful attack would compromise not just one application but every application utilizing the shared model. Just as regulators fear monocultures in supply chains, illustrated recently by Western fears that Huawei may become the only telecommunication network equipment vendor, regulators may need to pay more attention to monocultures of AI models that may permeate certain industries.

The second biggest threat predicted by the developer community, ransomware, was cited by just 19% of the survey participants. In response to these concerns, many governments have already taken steps to protect data privacy in an AI-driven landscape. They implement stringent access controls for sensitive information and adopt encryption techniques to prevent unauthorized access or breaches. Additionally, efforts are underway to develop comprehensive risk management strategies that anticipate potential threats before they occur.

While it may seem shocking that attackers would have access to the model, there are a number of common scenarios in which this would occur routinely. The model itself is just a digital file living on a computer, no different from an image or document, and therefore can be stolen like any other file on a computer. Because models are not always seen as highly sensitive assets, the systems holding these models may not have high levels of cybersecurity protection. History has shown that when software capabilities are commoditized, as they are becoming with AI systems, they are often not handled or invoked carefully in a security sense, as demonstrated by the prevalence of default root passwords. If this history is any indication, the systems holding these models will suffer from similar weaknesses that can lead to the model being easily stolen.

It is the first model scanning tool to support multiple model formats and is available for free to Data Scientists, ML Engineers, and AppSec professionals via Apache 2.0 licenses, to provide instant visibility into a key component of the ML lifecycle. It enables you to see, know, and manage security risks to defend against unique AI security threats, and embrace MLSecOps for a safer AI-powered world. With this announcement, the Mattermost platform is now supporting a new generation of AI solutions. The foundation of this expanding AI approach is “generative intelligence” augmentation, initially served through a customizable ChatGPT bot framework built to integrate with OpenAI, private cloud LLMs, as well as rising platforms, to embed generative AI assistance in collaborative workflows and automation.

DHS releases commercial generative AI guidance and is experimenting with building its own models

Technology has created entirely new streams of data and platforms that law enforcement is being called on to police,47 posing the challenge of analyzing a virtually infinite amount of content with a very finite amount of human resources. Much like the case with content filtering, the law enforcement community views the new generation of AI-enabled tools as necessary to keep pace with their expanding technological purview. The NIJ recognizes this potential for AI, stating, “Examining the huge volume of possibly relevant images and videos in an accurate and timely manner is a time-consuming, painstaking task, with the potential for human error due to fatigue and other factors.

SailPoint completes IRAP assessment boosting Australian government security – SecurityBrief Australia

SailPoint completes IRAP assessment boosting Australian government security.

Posted: Tue, 14 Nov 2023 08:00:00 GMT [source]

Microsoft conceded in a roundabout way in the announcement that some data will still be logged when government users tap into OpenAI models. The World’s first AI Bug Bounty Platform, huntr provides a single place for security researchers to submit vulnerabilities, to ensure the security and stability of AI applications. The Huntr community is the place for you to start your journey into AI threat research.

By completing this process, customers can ensure no logging data exists in Azure commercial. Microsoft’s Data, privacy, and Security for Azure OpenAI Service website provides detailed instructions and examples on modifying data logging settings. One concrete use case of generative AI that is currently working on is going beyond the capabilities of ChatGPT or Azure Open AI to create a better data exploration and harmonization process.

Secure and Compliant AI for Governments

The same goes for adoption of automated decision-making tools at the state and local levels. They’re used in law enforcement and the broader criminal legal cycle, in public benefit administration, in housing processes, and more. Certain states have pending legislation that would improve transparency and accountability of these tools state-wide, but none have passed yet. In recent years, government agencies have increasingly turned to cloud computing to manage vast amounts of data and streamline operations. While cloud technology has many benefits, it also poses security risks, especially when it comes to protecting sensitive information. To address these challenges, agencies are turning to a secure cloud fabric that can ensure the confidentiality, integrity, and availability of their data in the cloud.

In the absence of federal legislation by Congress on AI development and use, the Biden EO attempts to fill the gap in the most comprehensive manner possible while also calling on Congress to play its part and pass bipartisan legislation on privacy and AI technology. The potential of conversational AI to transform operations, services, and society is astounding — but only if we dare to harness it. With thoughtful implementation guided by ethics and equity from the start, governments can demonstrate AI’s immense capability to enhance lives while building vital public trust over time. Enable Agencies end-to-end connectivity and visibility across the entire development process from innovation to impact with measurable results.

Secure and Compliant AI for Governments

It enables governments worldwide to work towards a collective goal of ensuring that data privacy and security remain paramount in an increasingly interconnected world driven by artificial intelligence. The guidance shall include measures for the purposes listed in subsection 4.5(a) of this section. (d)  The Federal Acquisition Regulatory Council shall, as appropriate and consistent with applicable law, consider amending the Federal Acquisition Regulation to take into account the guidance established under subsection 4.5 of this section.

Secure and Compliant AI for Governments

Read more about Secure and Compliant AI for Governments here.

How can AI improve the economy?

AI has redefined aspects of economics and finance, enabling complete information, reduced margins of error and better market outcome predictions. In economics, price is often set based on aggregate demand and supply. However, AI systems can enable specific individual prices based on different price elasticities.

Why is artificial intelligence important in government?

By harnessing the power of AI, government agencies can gain valuable insights from vast amounts of data, helping them make informed and evidence-based decisions. AI-driven data analysis allows government officials to analyze complex data sets quickly and efficiently.

What is security AI?

AI security is evolving to safeguard the AI lifecycle, insights, and data. Organizations can protect their AI systems from a variety of risks and vulnerabilities by compartmentalizing AI processes, adopting a zero-trust architecture, and using AI technologies for security advancements.

Why is artificial intelligence important in national security?

Advances in AI will affect national security by driving change in three areas: military superiority, information superiority, and economic superiority. For military superiority, progress in AI will both enable new capabilities and make existing capabilities affordable to a broader range of actors.

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