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AI concerns continue as governments look for the right mix of regulations and protections FCW

In order to avoid the siloing of best practices and lessons learned within each department, agencies should place a priority on publishing their efforts openly and communicating findings outside of usual intra-agency pathways. Policymakers and relevant regulatory agencies should educate stakeholders about the threat landscape surrounding AI. This will allow stakeholders to make educated decisions regarding if AI is appropriate for their domain, as well as develop response plans for when attacks occur. Second, it should provide resources informing relevant parties about the steps they can take to protect against AI attacks from day one.

There is a growing global consensus that the most advanced AI systems require special attention. In July 2023, the Biden administration invited the leaders of seven frontier AI companies to the White House and had them voluntarily commit to a set of practices to increase the safety of their systems. 5 Production machine learning systems may feature a good amount of human and guard rail engineering, while others may be fully data dependent. As a result, some production systems may fall along a spectrum between “learned” systems that are fully data dependent and “designed” systems that are heavily based on hand-designed features. However, systems that are closer to the “designed” side of the spectrum may still be vulnerable to attacks, such as input attacks.

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For instance, Booz Allen identified that common cyber defense tools do not detect intrusion until 200 days after. In summary, AI in government enables authorities to enforce policies that result in better infrastructure monitoring to fight tax evasion and unlawful property changes. Manual administration is challenging and often proves insufficient in identifying land developments.

Further, because the government is turning to the private sector to develop its AI systems, compliance should be mandated as a precondition for companies selling AI systems to the government. Government applications for which truly no risk of attack exists, for example in situations where a successful attack would have no effect, can apply for a compliance waiver through a process that would review the circumstances and determine if a waiver is appropriate. Protecting against attacks that do not require intrusions will need to be based on profiling behavior that is indicative of formulating an attack. This will hold particularly true for the many AI applications that use open APIs to allow customers to utilize the models. Attackers can use this window into the system to craft attacks, replacing the need for more intrusive actions such as stealing a dataset or recreating a model. In this setting, it can be difficult to tell if an interaction with the system is a valid use of the system or probing behavior being used to formulate an attack.

New Initiative Seeks to Bring Collaboration to AI Security

Organizations should have vulnerability maps that document the assets their different AI systems share. This mapping should be rapid in the sense that once an asset or system is compromised, it should not require additional analysis to determine what other systems are compromised. For example, one such map would document which systems utilized the same training datasets. If this dataset was later compromised, administrators would immediately know what other systems are vulnerable and need to be addressed.

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• First, it is difficult to precisely specify what we want deep learning-based AI models to do, and to ensure that they behave in line with those specifications.
• Just as the FUSAG could expertly devise what patterns needed to be painted on the inflatable balloons to fool the Germans, with a type of AI attack called an “input attack,” adversaries can craft patterns of changes to a target that will fool the AI system into making a mistake.
• This is because these physical objects must first be digitized, for example with a camera or sensor, to be fed into the AI algorithm, a process that can destroy finer level detail.
• The public sector deals with large amounts of data, so increasing efficiency is key., AI and automation can help increase processing speed, minimize costs, and provide services to the public faster.

We’re SOC 2 Type 2 certified, but our commitment to ensuring our organization and those we serve meet evolving AI compliance guidelines doesn’t stop there. Our LLM platform for AI teams, deepset Cloud, is built with the highest security standards in mind. While the EU AI Act is not yet an active law, organizations working on new AI use cases should be aware of it as they develop their own AI systems, and build future-proof processes that ensure the traceability and documentation of systems created today. This part of the EO speaks of two big categories harnessing the benefits of AI to promote – firstly, healthcare and, secondly, transforming education. On one hand, the priority is to enhance the American healthcare system and develop affordable and life-saving drugs.

Why viAct is a pioneer in AI for Government & Public Sector?

A final recommended action plan should be ready no later than 12 months from its first convening. Because AI systems have already been deployed in critical areas, stakeholders and appropriate regulatory agencies should also retroactively apply these suitability tests to already deployed systems. Based on the outcome Secure and Compliant AI for Governments of the tests, the stakeholders or regulators should determine if any deployed AI systems are too vulnerable to attack for safe operation with their current level of AI use. Systems found to be too vulnerable should be promptly updated, and in certain cases taken offline until such updates are completed.

Red teaming is a form of adversarial model testing that attempts to identify undesirable behavior in an AI system, using methods such as prompt injection to expose the system’s latent biases. At Veriff, we’re constantly developing our technology to create the most advanced identity verification https://www.metadialog.com/governments/ solutions on the market – yet we never forget the vital role of human beings in our success. Our Senior Product Manager, Liisi German, explains why it’s important to unlock the potential of both AI and human intelligence to provide the best possible identity verification solutions to customers.

What is an Executive Order and how does it impact AI?

The Executive Order changes multiple agencies – including the NIST – on AI standards and technology implications for safety, security, and trust. However, starting small, focusing on citizen needs, and communicating benefits and limitations clearly can help agencies overcome barriers. The public sector can navigate obstacles to harness AI responsibly with proper care and partnerships. As government organizations pursue digital transformation, technologies like conversational AI will be critical to optimizing operational costs and delivering seamless citizen services. For example, Gartner predicts that by 2026, 60% of government organizations will prioritize business process automation through hyperautomation initiatives to support business and IT processes in government to deliver connected and seamless citizen services. Generative AI is impactful; it’s changing how the average office worker synthesizes information and creates content, and it’s not going away anytime soon, which means, local governments, just like their private sector counterparts, need policies and procedures for its safe, responsible and efficacious adoption.

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