AI governance: Key for addressing the Executive Order on safe, secure and trustworthy artificial intelligence
This paper highlights the ways in which state and local government can take advantage of generative AI while using it responsibly. In digitizing their services, government departments now hold highly confidential people, business and national data, and given the sensitivity of this data, they constantly face attacks from sophisticated, malicious actors. What’s more, it’s imperative their services are operational given their criticality and impact, hence the need for resilient and strong cyber practices. Azure Government customers, including US federal, state, and local government agencies and their partners, can now leverage the Microsoft Azure OpenAI Service. Purpose-built, AI-optimized infrastructure provides access to advanced generative models developed by OpenAI, such as GPT-4, GPT-3, and Embeddings.
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.
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. This article will highlight how AI-powered tools, like copilots, can streamline operations, boost productivity, and transform how citizens access services. We’ll cover everything from critical use cases to challenges to workforce implications. In a blog post shared exclusively with FedScoop that will publish Tuesday, Microsoft noted the of security and compliance required by government agencies when handling sensitive data. “To enable these agencies to fully realize the potential of AI, over the coming months Microsoft will begin rolling out new AI capabilities and infrastructure solutions across both our Azure commercial and Azure Government environments,” the blog post stated. Microsoft Azure Government maintains strict compliance standards to protect data, privacy, and security and provides an approval process to modify content filters and data logging.
Cybersecurity resolutions: how to make 2024 safer
Artificial intelligence, like Frankenstein’s monster, may appear human, but is decidedly not. Despite the popular warnings of sentient robots and superhuman artificial intelligence that grow more difficult to avoid with each passing day, artificial intelligence as it is today possesses no knowledge, no thought, and no intelligence. In the future, technical advancements may one day help us to better understand how machines can learn, and even learn how to embed these important qualities in technology.
Why is AI governance needed?
AI governance is needed in this digital technologies era for several reasons: Ethical concerns: AI technologies have the potential to impact individuals and society in significant ways, such as privacy violations, discrimination, and safety risks.
In the private sector, regulators should make compliance mandatory for high-risk uses of AI where attacks would have severe societal consequences, and optional for lower-risk uses in order to avoid disrupting innovation. With the EU AI Act expected to come into force in 2024, it’s clear that comprehensive AI legislation is on the horizon. In this article, we’ll highlight how many companies, including deepset, are already working hard to ensure that their generative technology offerings comply with existing and emerging regulations – and to provide users with the highest standard of security for their generative AI technology. 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. With careful adoption, conversational AI enables public sector agencies to deliver better services to citizens through automation and data-driven insights. The technology opens the door for more efficient, inclusive, and responsive governance.
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EPIC’s work is funded by the support of individuals like you, who help us to continue to protect privacy, open government, and democratic values in the information age. Leverage a leading enterprise Agile planning solution to scale Agile best practices and gain the flexibility to modernize application delivery without the need to replace existing technology. CMMC 2.0 is expected to become the official standard for cybersecurity certification in… Manually copying data from various spreadsheets and word documents when running an audit or assessment to produce static reports is extremely time-consuming, error prone and inefficient. The tech giant’s Teams Premium service with intelligent recap of meetings is expected to roll out to government users during the spring of 2024. Intelligent recap uses AI to help users summarize meeting content and focus on key elements through AI-generated meeting notes and tasks.
(ii) as part of the AI Tech Sprint competitions and in collaboration with appropriate partners, provide participants access to technical assistance, mentorship opportunities, individualized expert feedback on products under development, potential contract opportunities, and other programming and resources. Independent regulatory agencies are encouraged, as they deem appropriate, to consider whether to mandate guidance through regulatory action in their areas of authority and responsibility. Accelerate content creation, communication and understanding with our GDPR-compliant AI content platform that writes in your tone of voice.Our AI content tool ensures that all data is handled and protected in compliance with GDPR regulations. AI can be a fundamental source of competitive advantage, helping organizations meet challenges and uncover opportunities for now and in the future.
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. For more information on federal programs and policy on artificial intelligence, visit ai.gov. Public sector organizations embracing conversational AI stand to be further ahead of their counterparts due to the technology’s ability to optimize operational costs and provide seamless services to its citizens. NB Defense is an open source offering for Jupyter notebooks that quickly scans notebook(s) for common security issues, identifies potential risks, and guides your remediation. Available now, this tool helps your teams quickly get started with protecting your AI from risks. At Securiti, our mission is to enable enterprises to safely harness the incredible power of data and the cloud by controlling the complex security, privacy and compliance risks.
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.
In the context of military operations in armed conflict, the United States believes that international humanitarian law (IHL) provides a robust and appropriate framework for the regulation of all weapons, including those using autonomous functions provided by technologies such as AI. Building a better common understanding of the potential risks and benefits that are presented by weapons with autonomous functions, in particular their potential to strengthen compliance with IHL and mitigate risk of harm to civilians, should be the focus of international discussion. The United States supports the progress in this area made by the Convention on Certain Conventional Weapons, Group of Governmental Experts on Emerging Technologies in the Area of Lethal Autonomous Weapon Systems (GGE on LAWS), which adopted by consensus 11 Guiding Principles on responsible development and use of LAWS in 2019. The State Department will continue to work with our colleagues at the Department of Defense to engage the international community within the LAWS GGE. Offering multiple foundation models (FMs) is especially important to the public sector, which comprises governments, educational institutions, nonprofits, aerospace entities, and health care organizations that are exploring how to use generative AI to satisfy the evolving needs of citizens around the globe. We’re excited that AWS Partners are using these technologies to address challenges like securely managing complex data sets, detecting cybersecurity threats, and more.
(ff) The term “testbed” means a facility or mechanism equipped for conducting rigorous, transparent, and replicable testing of tools and technologies, including AI and PETs, to help evaluate the functionality, usability, and performance of those tools or technologies. (ee) The term “synthetic content” means information, such as images, videos, audio clips, and text, that has been significantly modified or generated by algorithms, including by AI. (h) The term “critical and emerging technologies” means those technologies listed in the February 2022 Critical and Emerging Technologies List Update issued by the National Science and Technology Council (NSTC), as amended by subsequent updates to the list issued by the NSTC. Typetone AI allows you to streamline your workflow by integrating with any existing system you use, such as CMS, ATS, CRM, and more.
By switching valid data with poisoned data, the machine learning model underpinning the AI system itself becomes poisoned during the learning process. As a toy example of this type of poisoning attack, consider training a facial recognition-based security system that should admit Alice but reject Bob. If an attacker poisons the dataset by changing some of the images of “Alice” to ones of “Bob,” the system would fail in its mission because it would learn to identify Bob as Alice. Therefore Bob would be incorrectly authenticated as Alice when the system was deployed.
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In the future, red teaming will be essential for any high-risk AI system, not just the foundational models. In the near future, AI systems will have specific requirements depending on the domain in which they’re deployed. The EO introduces a wide range of critical guidelines aimed at the privacy, security, and safety of AI technologies. With care, transparency, and responsible leadership, conversational AI can unlock a brighter future, one where high-quality public services are profoundly more accessible, inclusive, and personalized for all. With planning, government workforces can be augmented and empowered by conversational AI rather than displaced. Change management and inclusive policies that support workers will enable the public sector to tap the full potential of AI while ensuring no one is left behind.
- AI Governance also facilitates compliance with industry-specific regulations, such as HIPAA for healthcare or FINRA for financial services.
- In many contexts, these assets are currently not treated as secure assets, but rather as “soft” assets lacking in protection.
- If confronted with better content filters, they are likely to be the first adopters of AI attacks against these filters.
- The past decade has borne poisonous fruit from technological seeds planted before the turn of the century.
Algorithms provide the rules and context for AI as it begins to sort and analyze data, providing structure as AI learns. Domino also gives code-first data scientists and researchers the flexibility and freedom to support what they do best — solve problems without technical hurdles. Domino’s model factory eliminates backlogs by automating model governance, validation, production, monitoring and performance tracking. And Domino centralizes AI workflows, so agencies always know which models are being consumed so they can stay aligned with sponsors and measure impact. Making AI a trustworthy tool for decision-making requires traceability to ensure accountability, observability to remove model biases and limitations, and enterprise-grade governance so models are responsibly built from day one. Individuals should take appropriate steps to ensure the security of their devices and accounts.
The military will need to develop protocols that prioritize early identification of when its AI algorithms have been hacked or attacked so that these compromised systems can be replaced or re-trained immediately. Hardening these “soft” targets will be an integral component of defending against AI attacks. This is because the two prominent forms of AI attacks discussed here, input and poisoning attacks, are easier to execute if the attacker has access to some component of the AI system and training pipeline. This has transformed a wide range of assets that span the AI training and implementation pipelines into targets for would-be attackers. Specifically, these assets include the datasets used to train the models, the algorithms themselves, system and model details such as which tools are used and the structure of the models, storage and compute resources holding these assets, and the deployed AI systems themselves.
Is AI a security risk?
AI tools pose data breach and privacy risks.
AI tools gather, store and process significant amounts of data. Without proper cybersecurity measures like antivirus software and secure file-sharing, vulnerable systems could be exposed to malicious actors who may be able to access sensitive data and cause serious damage.
Unlike humans, machine do not tire.”48 Beyond just its use in keeping pace with expanding amounts of content, AI can be used to provide more effective policing and crime prevention by detecting criminal warning signs earlier and apprehending suspects faster. Beyond the threats posed by sharing datasets, the military may also seek to re-use and share models and the tools used to create them. Because the military is a, if not the, prime target for cyber theft, the models and tools themselves will also become targets for adversaries to steal through hacking or counterintelligence operations. History has shown that computer systems are an eternally vulnerable channel that can be reliably counted on as an attack avenue by adversaries.
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. The existence of the possibility that data will be collected may be enough of a threat to execute this type of influence campaign. Beyond this supportive role, regulators should affirm that they will use an entity’s effort in executing a suitability test in deciding culpability and responsibility if attacks do occur.
Read more about Secure and Compliant AI for Governments here.
How AI can be used in government?
The federal government is leveraging AI to better serve the public across a wide array of use cases, including in healthcare, transportation, the environment, and benefits delivery. The federal government is also establishing strong guardrails to ensure its use of AI keeps people safe and doesn't violate their rights.
What is AI in governance?
AI governance is the ability to direct, manage and monitor the AI activities of an organization. This practice includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits.
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.