Government Safe AI content creation

In Maryland, the Algorithmic Decision Systems Procurement and Discriminatory Act was proposed in February 2021 to require that if a state unit purchases a product or service that includes an algorithmic decision system, it must adhere to responsible AI standards. They must also evaluate the system’s impact and potential risks, paying particular attention to potential discrimination. Further, state units must ensure the system adheres to transparency commitments, including disclosing the system’s capabilities, limitations, and potential problems to the state. Harmful consequences from AI systems can ensue for several reasons, even if neither the user nor the developer intends harm. 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. In other words, reliably controlling AI models’ behavior remains a largely unsolved technical problem.

Microsoft said it won’t be specifically using government data to train OpenAI models, so there’s likely no chance that top secret data ends up being spilled in a response meant for someone else. Microsoft conceded in a roundabout way in the announcement that some data will still be logged when government users tap into OpenAI models. AI systems require robust, secure and reliable infrastructure and compatibility with existing systems and platforms.

🔥 OpenAI’s identity crisis and the battle for AI’s future

The move sets out the government’s intentions to regulate and further advance the growth of AI technology in the years ahead. In Microsoft, there’s a service called Azure Open AI on your data, and some government agencies have connected that their own SharePoint repositories to begin to perform some of the capabilities you would expect Copilot to have with their data. This allows organizations to experiment and learn how Copilot works and raises questions about how it can revolutionize government tenants. Artificial intelligence (AI) is rapidly transforming businesses and industries, and the potential for AI in government is massive – it can automate tedious tasks, improve public services, and even reduce costs.

In many applications, data is neither considered nor treated as confidential or classified, and may even be widely and openly shared. A regular object is altered with a visible attack pattern (a few pieces of tape) to form the attack object. While the regular object would be classified correctly by the AI system, the attack object is incorrectly classified as a “green light”. Now that we have an understanding of why these attacks are possible, we now turn our attention to looking at actual examples of these attacks. Given the unparalleled success of AI over the past decade, it is surprising to learn that these attacks are possible, and even more so, that they have not yet been fixed.

Why is artificial intelligence important for security?

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. Through the service, government agencies will get access to ChatGPT use cases without sacrificing “the stringent security and compliance standards they need to meet government requirements for sensitive data,” Microsoft explained in a canned statement. For this reason, local government agencies and elected officers must become vigilant, proactive, and responsible stewards of AI – by addressing security concerns, regulatory concerns, and public safety concerns in a holistic way.

• 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.
• There are other scenarios in which intrusion detection will be significantly more difficult.
• The RCN shall serve to enable privacy researchers to share information, coordinate and collaborate in research, and develop standards for the privacy-research community.
• The growing use of AI technologies has pointed to the fact that governments around the world face similar challenges concerning the protection of citizens’ personal information.
• It argues that AI attacks constitute a new vertical of attacks distinct in nature and required response from existing cybersecurity vulnerabilities.

As a result, “attacks” on these systems, from a US-based policy view of promoting human rights and free expression, would not be an “attack” in a negative sense of the word. Instead, these AI “attacks” would become a source of protection capable of promoting safety and freedom in the face of oppressive AI systems instituted by the state. In order to properly regulate commercial firms in this domain, policymakers must understand how this commercial development of AI systems will progress. In one scenario, individual companies will each build their own proprietary AI systems. Because each company is building its own system, industries cannot pool resources to invest in preventative measures and shared expertise. However, this diversification limits the applicability of an attack on one AI system to be applied broadly to many other systems.

How will artificial intelligence and security evolve in the future?

Many AI attacks are aided by gaining access to assets such as datasets or model details. In many scenarios, doing so will utilize traditional cyberattacks that compromise the confidentiality and integrity of systems, a subject well studied within the cybersecurity CIA triad. Traditional confidentiality attacks will enable adversaries to obtain the assets needed to engineer input attacks.

Agencies need to secure the IP their contractors create in order to reuse, evolve and maintain their models over time. They also need to enable collaboration across contractors, users and datasets, and give contractors access to their preferred tools. One major step is the enactment of strict laws and regulations governing the collection, storage, and use of individuals’ personal data. Governments have introduced comprehensive frameworks that outline organizations’ responsibilities in handling sensitive information. These regulations often include requirements for obtaining consent from individuals before collecting their data, as well as guidelines on how long such information can be retained. If classified or confidential information falls into the enemy’s hands, it could lead to a compromise of intelligence operations and expose the vulnerability of a country’s infrastructure.

Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence

This would therefore allow the adversary to craft an attack without ever having to compromise the original dataset or model. As a result, if this application was deemed easy to attack, an AI system may not be well suited to this particular application. Compliance programs will accomplish these goals by encouraging stakeholders https://www.metadialog.com/governments/ to adopt a set of best practices in securing their systems and making them more robust against AI attacks. These best practices manage the entire lifecycle of AI systems in the face of AI attacks. In the planning stage, they will force stakeholders to consider attack risks and surfaces when planning and deploying AI systems.

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. 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. It is safe to say that the Executive Order issued by the Biden administration is indeed one of the most comprehensive directives ever introduced for AI governance, development, and regulation by any government in the world.

It can automate crucial processes like records management and ensure that tasks are carried out in compliance with industry governance protocols and standards and can restrict access to sensitive data in an organization. For customers, AI can help detect and prevent fraud by analyzing records and transactions to learn normal behaviors and detecting outliers. Today, we see more infusions of AI into government processes, and challenges around data privacy and security have become the core of several conversations.

(c)  This order is not intended to, and does not, create any right or benefit, substantive or procedural, enforceable at law or in equity by any party against the United States, its departments, agencies, or entities, its officers, employees, or agents, or any other person. (iv)   recommendations for the Department of Defense and the Department of Homeland Security to work together to enhance the use of appropriate authorities for the retention of certain noncitizens of vital importance to national security by the Department of Defense and the Department of Homeland Security. (C)  disseminates those recommendations, best practices, or other informal guidance to appropriate stakeholders, including healthcare providers.

Many of the current regulations are still being drafted and are therefore often vague in their exact reporting and documentation requirements. However, it is expected that the EU AI law will include several documentation requirements for AI systems that disclose the exact process that went into their creation. This will likely include the origin and lineage of data, details of model training, experiments conducted, and the creation of prompts. Thanks to large language models like GPT-4, not only is there more AI-generated content – it is also increasingly hard to distinguish from human-generated content.

Artificial intelligence (AI) – especially generative AI like OpenAI’s ChatGPT and DALL-E models – is rapidly entering offices and enterprise systems that power many industries, from finance and healthcare to education and transportation. 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.

New methods will be needed to allow for audits of systems without compromising security, such as restricting audits to a trusted third party rather than publishing openly. Response plans should be based on the best efforts to respond to attacks and control the amount of damage. Continuing the social network example, sites relying on content filtering may need response plans that include the use of other methods, such as human-based content auditing, to filter content. 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.