How AWS Partners are advancing generative AI for government, health care, and other public sector organizations

Additionally, governments are establishing specialized agencies or departments responsible for overseeing data protection efforts. These entities monitor compliance with regulations, conduct audits, and enforce penalties for non-compliance. By having dedicated bodies focused on data privacy and security matters, governments ensure accountability and provide a mechanism for addressing any breaches or lapses promptly. 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.

In terms of implementing these suitability tests, regulators should play a supportive role. In areas requiring more regulatory oversight, regulators should write domain specific tests and evaluation metrics to be used. In areas requiring less regulatory oversight, they should write general guidelines to be followed. Beyond this, regulators should provide advice and counsel where needed, both in helping entities answer the questions that make up the tests as well as in forming a final implementation decision. Like AI attacks, the technology behind Deepfakes shares a similar if not even more advanced technical sophistication. However, despite the technique living at the intersection of cutting-edge AI, computer vision, and image processing research, large number of amateurs with no technical background were able to use the method to produce the videos.

EPIC Comments: National Institute of Standards and Technology AI Risk Management Framework

Governments should collaborate on policy frameworks that promote transparency, accountability, and responsible use of AI technologies. By sharing best practices and working together on common challenges, countries can collectively establish a more secure environment for citizens’ data. Another important step is fostering international cooperation on data privacy and security issues.

The function of the White House AI Council is to coordinate the activities of agencies across the Federal Government to ensure the effective formulation, development, communication, industry engagement related to, and timely implementation of AI-related policies, including policies set forth in this order. (iv)   take such steps as are necessary and appropriate, consistent with applicable law, to support and advance the near-term actions and long-term strategy identified https://www.metadialog.com/governments/ through the RFI process, including issuing new or updated guidance or RFIs or consulting other agencies or the Federal Privacy Council. (ii)   After principles and best practices are developed pursuant to subsection (b)(i) of this section, the heads of agencies shall consider, in consultation with the Secretary of Labor, encouraging the adoption of these guidelines in their programs to the extent appropriate for each program and consistent with applicable law.

Championing Individuals’ Privacy Protection

(C)  launching an initiative to create guidance and benchmarks for evaluating and auditing AI capabilities, with a focus on capabilities through which AI could cause harm, such as in the areas of cybersecurity and biosecurity. (v)   The term “national security system” has the meaning set forth in 44 U.S.C. 3552(b)(6). (l)  The term “Federal law enforcement agency” has the meaning set forth in section 21(a) of Executive Order of May 25, 2022 (Advancing Effective, Accountable Policing and Criminal Justice Practices To Enhance Public Trust and Public Safety).

While safety is critical, some argue that government regulation of AI could also serve as a “wolf in sheep’s clothing” — a means to consolidate control over AI gains in the hands of a few. As Yann LeCun recently called out, leaders of major AI companies including Altman, Hassabis, and Amodei may be motivated by regulatory capture more than broad safety concerns. Andrew Ng has made similar arguments that there is a financial incentive to spread fear. It’s imperative to ensure any government oversight balances both safety and open-source as AI capabilities advance.

Cyber-attacks and data breaches can cost organizations–in dollars and in customer trust. In the future, AI will continue to automate processes and improve detection for fraud and security risks, making it easier for businesses to stay in compliance with regulations and keep important data secure. But, every organization looking to use artificial intelligence must take care to implement it responsibly and ethically. In the next decades, we will see the rise of AI especially in industries that rely extensively on personal data like healthcare and finance.

As discussed in the mitigation stage compliance requirements below, system operators should have a predetermined plan that specifies exactly the actions that should be taken in the case of system compromise, and put the plan into action immediately. Improve intrusion detection systems to better detect when assets have been compromised and to detect patterns of behavior indicative of an adversary formulating an attack. If key types of data are either missing from or not sufficiently represented in a collected dataset, the resulting AI system will not be able to function properly when it encounters situations not represented in its dataset. If the adversary controls the entities on which data is being collected, they can manipulate them to influence the data collected. Because the adversary has control over their own aircraft, it can alter them in order to alter the data collected. Adversaries need not be aware that data is being collected in order to manipulate the process.

Preparing public sector workforces for AI transformation

The Secretary of Transportation shall further encourage ARPA-I to prioritize the allocation of grants to those opportunities, as appropriate. The work tasked to ARPA-I shall include soliciting input on these topics through a public consultation process, such as an RFI. (v)    establish an office to coordinate development of AI and other critical and emerging technologies across Department of Energy programs and the 17 National Laboratories. Such standards and procedures may include a finding by the Secretary that such foreign reseller, account, or lessee complies with security best practices to otherwise deter abuse of United States IaaS Products. (ii)  Companies, individuals, or other organizations or entities that acquire, develop, or possess a potential large-scale computing cluster to report any such acquisition, development, or possession, including the existence and location of these clusters and the amount of total computing power available in each cluster. (p)  The term “generative AI” means the class of AI models that emulate the structure and characteristics of input data in order to generate derived synthetic content.

If a large number of applications depended on this same shared dataset, this could lead to widespread vulnerabilities throughout the military. In the case of input attacks, an adversary would then be easily able to find attack patterns to engineer an attack on any systems trained using the dataset. In the case of poisoning attacks, an adversary would only need to compromise one dataset in order Secure and Compliant AI for Governments to poison any downstream models that are later trained using this poisoned dataset. Military applications of AI are expected to be a critical component of the next major war. The U.S. Department of Defense has recently made the integration of artificial intelligence and machine learning into the military a high priority with its creation of the Joint Artificial Intelligence Center (JAIC).

The guidelines themselves instead urge companies to consider the potential damage to the business that insecure AI models might cause. “Where system compromise could lead to tangible or widespread physical or reputational damage, significant loss of business operations, leakage of sensitive or confidential information and/or legal implications, AI cyber security risks should be treated as critical,” the guidelines state. Effectively regulating the use of frontier AI, intervening as close as possible to the harm, can address many of the relevant challenges. Many of AI’s harmful uses are already illegal, from criminal impersonation to cyberattacks and the release of dangerous pathogens. And there are certainly additional steps we can take to make it harder to use AI capabilities for ill.

Another—known as a poisoning attack—can stop an AI system from operating correctly in situations, or even insert a backdoor that can later be exploited by an adversary. 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. However, just as not all applications of AI are “good,” not all AI attacks are necessarily “bad.” As autocratic regimes turn to AI as a tool to monitor and control their populations, AI “attacks” may be used as a protective measure against government oppression, much like technologies such as Tor and VPNs are. Renewables are widely perceived as an opportunity to shatter the hegemony of fossil fuel-rich states and democratize the energy landscape. Virtually all countries have access to some renewable energy resources (especially solar and wind power) and could thus substitute foreign supply with local resources.

Input Attacks

ModelScan is an open source project used to determine if ML Models contain unsafe code. 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 section, the President addresses one of the hottest topics for the American population – protecting workers’ rights from AI harm. This clause has raised the concern that AI is changing America’s jobs and workplaces, promising improved productivity. In addition, the dangers of increased workplace surveillance, bias, and job displacement are becoming more frequent and prominent.

Government agencies must adopt and enforce ethical AI guidelines in different phases of the AI lifecycle to ensure transparency, contestability, and accountability. However, most public-sector AI initiatives are underfunded and understaffed to execute ethical AI policies effectively. Similarly, the Department of Homeland Security, USA, uses EMMA, a virtual assistant catering to immigration services. EMMA guides around one million applicants per month regarding the various services offered by the department and directs them to relevant pages and resources. AI-based cognitive automation, such as rule-based systems, speech recognition, machine translation, and computer vision, can potentially automate government tasks at unprecedented speed, scale, and volume.