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Risk Management
AI Attacks AI Policy AI Risks Company Security Cyber Safety Cyber Security Cybersecurity Crisis Risk Management

Addressing AI Risks: Best Practices for Proactive Crisis Management

 

An essential element of effective crisis management is preparing for both visible and hidden risks. A recent report by Riskonnect, a risk management software provider, warns that companies often overlook the potential threats associated with AI. Although AI offers tremendous benefits, it also carries significant risks, especially in cybersecurity, which many organizations are not yet prepared to address. The survey conducted by Riskonnect shows that nearly 80% of companies lack specific plans to mitigate AI risks, despite a high awareness of threats like fraud and data misuse. 

Out of 218 surveyed compliance professionals, 24% identified AI-driven cybersecurity threats—like ransomware, phishing, and deepfakes — as significant risks. An alarming 72% of respondents noted that cybersecurity threats now severely impact their companies, up from 47% the previous year. Despite this, 65% of organizations have no guidelines on AI use for third-party partners, often an entry point for hackers, which increases vulnerability to data breaches. Riskonnect’s report highlights growing concerns about AI ethics, privacy, and security. Hackers are exploiting AI’s rapid evolution, posing ever-greater challenges to companies that are unprepared. 

Although awareness has improved, many companies still lag in adapting their risk management strategies, leaving critical gaps that could lead to unmitigated crises. Internal risks can also impact companies, especially when they use generative AI for content creation. Anthony Miyazaki, a marketing professor, emphasizes that while AI-generated content can be useful, it needs oversight to prevent unintended consequences. For example, companies relying on AI alone for SEO-based content could risk penalties if search engines detect attempts to manipulate rankings. 

Recognizing these risks, some companies are implementing strict internal standards. Dell Technologies, for instance, has established AI governance principles prioritizing transparency and accountability. Dell’s governance model includes appointing a chief AI officer and creating an AI review board that evaluates projects for compliance with its principles. This approach is intended to minimize risk while maximizing the benefits of AI. Empathy First Media, a digital marketing agency, has also taken precautions. It prohibits the use of sensitive client data in generative AI tools and requires all AI-generated content to be reviewed by human editors. Such measures help ensure accuracy and alignment with client expectations, building trust and credibility. 

As AI’s influence grows, companies can no longer afford to overlook the risks associated with its adoption. Riskonnect’s report underscores an urgent need for corporate policies that address AI security, privacy, and ethical considerations. In today’s rapidly changing technological landscape, robust preparations are necessary for protecting companies and stakeholders. Developing proactive, comprehensive AI safeguards is not just a best practice but a critical step in avoiding crises that could damage reputations and financial stability.
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Threat Landscape
AI Risks Artificial Intelligence MIT Technology Threat Landscape

MIT Database Lists Hundreds of AI Dangers Impacting Human Lives

 

Artificial intelligence is present everywhere. If it isn't powering your online search results, it's just a click away with your AI-enabled mouse. If it's not helping you enhance your LinkedIn profile, it's benefiting you at work. As AIs become more intelligent, outspoken voices warn of the technology's potential risks. 

These range from literally replacing you at your job to even more terrifying end-of-the-world circumstances. The Massachusetts Institute of Technology is aware of these competing currents and has compiled a list of the ways it believes AI might pose challenges. 

AI threats

In an article supporting the research, MIT summarised the several ways AI could endanger society. Humans outperform artificial intelligence. Kind of. While 51% of the threats were attributed directly to AIs, 34% originated with humans using AI technology--there are some evil individuals out there, remember. 

However, approximately two thirds of the risks were identified after an AI had been trained and deployed, compared to 10% before that point. This provides significant support to AI regulatory initiatives, as it coincides with the announcement that OpenAI and Anthropic would submit their new, smartest AIs to the US AI Safety Institute for testing before releasing them to the public. 

So, what are the AI risks? A quick search of the database reveals some alarming category types. One scenario involves AI harm emerging as a "side effect of a primary goal like profit or influence," in which AI makers "wilfully allow it to cause widespread social damage like pollution, resource depletion, mental illness, misinformation, or injustice." Similarly, additional side effects occur when "one or more criminal entities" construct an AI to "intentionally inflict harm, such as for terrorism or combating law enforcement.” 

Other threats that MIT has identified feel more in line with current news reports, especially with regard to election misinformation, even though these seem more suited for science fiction dystopias: AIs could be harmful when "extensive data collection" in the models "brings toxic content and stereotypical bias into the training data." 

One of the other concerns is that AI systems have the potential to become "very invasive of people's privacy, controlling, for instance, the length of someone's last romantic relationship." This is a type of soft power control where society is steered by small adjustments; it is similar to some of the concerns raised by US authorities on the possible impact of TikTok's algorithm.
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USA
Advanced Technology AI Risks AI Safety AI Systems AI technology Innovation NIST Technology USA

NIST Introduces ARIA Program to Enhance AI Safety and Reliability

 

The National Institute of Standards and Technology (NIST) has announced a new program called Assessing Risks and Impacts of AI (ARIA), aimed at better understanding the capabilities and impacts of artificial intelligence. ARIA is designed to help organizations and individuals assess whether AI technologies are valid, reliable, safe, secure, private, and fair in real-world applications. 

This initiative follows several recent announcements from NIST, including developments related to the Executive Order on trustworthy AI and the U.S. AI Safety Institute's strategic vision and international safety network. The ARIA program, along with other efforts supporting Commerce’s responsibilities under President Biden’s Executive Order on AI, demonstrates NIST and the U.S. AI Safety Institute’s commitment to minimizing AI risks while maximizing its benefits. 

The ARIA program addresses real-world needs as the use of AI technology grows. This initiative will support the U.S. AI Safety Institute, expand NIST’s collaboration with the research community, and establish reliable methods for testing and evaluating AI in practical settings. The program will consider AI systems beyond theoretical models, assessing their functionality in realistic scenarios where people interact with the technology under regular use conditions. This approach provides a broader, more comprehensive view of the effects of these technologies. The program helps operationalize the framework's recommendations to use both quantitative and qualitative techniques for analyzing and monitoring AI risks and impacts. 

ARIA will further develop methodologies and metrics to measure how well AI systems function safely within societal contexts. By focusing on real-world applications, ARIA aims to ensure that AI technologies can be trusted to perform reliably and ethically outside of controlled environments. The findings from the ARIA program will support and inform NIST’s collective efforts, including those through the U.S. AI Safety Institute, to establish a foundation for safe, secure, and trustworthy AI systems. This initiative is expected to play a crucial role in ensuring AI technologies are thoroughly evaluated, considering not only their technical performance but also their broader societal impacts. 

The ARIA program represents a significant step forward in AI oversight, reflecting a proactive approach to addressing the challenges and opportunities presented by advanced AI systems. As AI continues to integrate into various aspects of daily life, the insights gained from ARIA will be instrumental in shaping policies and practices that safeguard public interests while promoting innovation.
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Technology
AI Risks Artifcial Intelligence Policymakers Tech Companies Technology

Geoffrey Hinton Discusses Risks and Societal Impacts of AI Advancements

 


Geoffrey Hinton, often referred to as the "godfather of artificial intelligence," has expressed grave concerns about the rapid advancements in AI technology, emphasising potential human-extinction level threats and significant job displacement. In an interview with BBC Newsnight, Hinton warned about the dangers posed by unregulated AI development and the societal repercussions of increased automation.

Hinton underscored the likelihood of AI taking over many mundane jobs, leading to widespread unemployment. He proposed the implementation of a universal basic income (UBI) as a countermeasure. UBI, a system where the government provides a set amount of money to every citizen regardless of their employment status, could help mitigate the economic impact on those whose jobs are rendered obsolete by AI. "I advised people in Downing Street that universal basic income was a good idea," Hinton revealed, arguing that while AI-driven productivity might boost overall wealth, the financial gains would predominantly benefit the wealthy, exacerbating inequality.

Extinction-Level Threats from AI

Hinton, who recently left his position at Google to speak more freely about AI dangers, reiterated his concerns about the existential risks AI poses. He pointed to the developments over the past year, indicating that governments have shown reluctance in regulating the military applications of AI. This, coupled with the fierce competition among tech companies to develop AI products quickly, raises the risk that safety measures may be insufficient.

Hinton estimated that within the next five to twenty years, there is a significant chance that humanity will face the challenge of AI attempting to take control. "My guess is in between five and twenty years from now there’s a probability of half that we’ll have to confront the problem of AI trying to take over," he stated. This scenario could lead to an "extinction-level threat" as AI progresses to become more intelligent than humans, potentially developing autonomous goals, such as self-replication and gaining control over resources.

Urgency for Regulation and Safety Measures

The AI pioneer stressed the need for urgent action to regulate AI development and ensure robust safety measures are in place. Without such precautions, Hinton fears the consequences could be dire. He emphasised the possibility of AI systems developing motivations that align with self-preservation and control, posing a fundamental threat to human existence.

Hinton’s warnings serve as a reminder of the dual-edged nature of technological progress. While AI has the potential to revolutionise industries and improve productivity, it also poses unprecedented risks. Policymakers, tech companies, and society at large must heed these warnings and work collaboratively to harness AI's benefits while mitigating its dangers.

In conclusion, Geoffrey Hinton's insights into the potential risks of AI push forward the need for proactive measures to safeguard humanity's future. His advocacy for universal basic income reflects a pragmatic approach to addressing job displacement, while his call for stringent AI regulation highlights the urgent need to prevent catastrophic outcomes. As AI continues to transform, the balance between innovation and safety will be crucial in shaping a sustainable and equitable future.


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Technology
AI Model AI Risks Generative AI Machine learning Technology

Here's How to Choose the Right AI Model for Your Requirements

 

When kicking off a new generative AI project, one of the most vital choices you'll make is selecting an ideal AI foundation model. This is not a small decision; it will have a substantial impact on the project's success. The model you choose must not only fulfil your specific requirements, but also be within your budget and align with your organisation's risk management strategies. 

To begin, you must first determine a clear goal for your AI project. Whether you want to create lifelike graphics, text, or synthetic speech, the nature of your assignment will help you choose the proper model. Consider the task's complexity as well as the level of quality you expect from the outcome. Having a specific aim in mind is the first step towards making an informed decision.

After you've defined your use case, the following step is to look into the various AI foundation models accessible. These models come in a variety of sizes and are intended to handle a wide range of tasks. Some are designed for specific uses, while others are more adaptable. It is critical to include models that have proven successful in tasks comparable to yours in your consideration list. 

Identifying correct AI model 

Choosing the proper AI foundation model is a complicated process that includes understanding your project's specific demands, comparing the capabilities of several models, and taking into account the operational context in which the model will be implemented. This guide synthesises the available reference material and incorporates extra insights to provide an organised method to choosing an AI base model. 

Identify your project targets and use cases

The first step in choosing an AI foundation model is to determine what you want to achieve with your project. Whether your goal is to generate text, graphics, or synthetic speech, the nature of your task will have a considerable impact on the type of model that is most suitable for your needs. Consider the task's complexity and the desired level of output quality. A well defined goal will serve as an indicator throughout the selecting process. 

Figure out model options 

Begin by researching the various AI foundation models available, giving special attention to models that have proven successful in jobs comparable to yours. Foundation models differ widely in size, specialisation, and versatility. Some models are meant to specialise on specific functions, while others have broader capabilities. This exploratory phase should involve a study of model documentation, such as model cards, which include critical information about the model's training data, architecture, and planned use cases. 

Conduct practical testing 

Testing the models with your specific data and operating context is critical. This stage ensures that the chosen model integrates easily with your existing systems and operations. During testing, assess the model's correctness, dependability, and processing speed. These indicators are critical for establishing the model's effectiveness in your specific use case. 

Deployment concerns 

Make the deployment technique choice that works best for your project. While on-premise implementation offers more control over security and data privacy, cloud services offer scalability and accessibility. The decision you make here will mostly depend on the type of application you're using, particularly if it handles sensitive data. In order to handle future expansion or requirements modifications, take into account the deployment option's scalability and flexibility as well. 

Employ a multi-model strategy 

For organisations with a variety of use cases, a single model may not be sufficient. In such cases, a multi-model approach can be useful. This technique enables you to combine the strengths of numerous models for different tasks, resulting in a more flexible and durable solution. 

Choosing a suitable AI foundation model is a complex process that necessitates a rigorous understanding of your project's requirements as well as a thorough examination of the various models' characteristics and performance. 

By using a structured approach, you can choose a model that not only satisfies your current needs but also positions you for future advancements in the rapidly expanding field of generative AI. This decision is about more than just solving a current issue; it is also about positioning your project for long-term success in an area that is rapidly growing and changing.
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Technology
AI Jobs AI Potentials AI Risks Artificial Intelligence Frontier AI Rishi Sunak Tech Summit Technology

Rishi Sunak Outlines Risks and Potential of AI Ahead of Tech Summit


UK Prime Minister Rishi Sunak has warned against the use of AI, as it could be used to design chemical and biological weapons. He says that, in the worst case scenario, people are likely to lose all control over AI, preventing it from turning off. 

However, he notes that while the potential for harm in AI usage is disputed, “we must not put heads in the sand,” over AI risks.

Sunak notes that the technology is already creating new job opportunities and that its advancement would catalyze economic growth and productivity, though he acknowledged that it would have an impact on the labor market.

“The responsible thing for me to do is to address those fears head on, giving you the peace of mind that we will keep you safe, while making sure you and your children have all the opportunities for a better future that AI can bring[…]Doing the right thing, not the easy thing, means being honest with people about the risks from these technologies,” Sunak stated. On Wednesday, the government had released documents highlighting the risks of AI. 

Existential risks from the technology cannot be ruled out, according to one research on the future risks of frontier AI, the term given to frontier AI systems will be discussed at the summit. 

“Given the significant uncertainty in predicting AI developments, there is insufficient evidence to rule out that highly capable Frontier AI systems, if misaligned or inadequately controlled, could pose an existential threat.”

The paper also presents several concerning scenarios about the advancement of AI.

One warns of the potential backlash from the public, as their jobs are being taken by AI. “AI systems are deemed technically safe by many users … but they are nevertheless causing impacts like increased unemployment and poverty,” says the paper, creating a “fierce public debate about the future of education and work”.

In another case mentioned in the document, dubbed as the ‘Wild West,’ the illicit use of AI to commit fraud and scams leads to social instability as a result of numerous victims of organized crime, widespread trade secret theft by enterprises, and an increase in the amount of AI-generated content that clogs the internet.

“This could lead to ‘personalised’ disinformation, where bespoke messages are targeted at individuals rather than larger groups and are therefore more persuasive,” said the discussion document, cautioning of the potential decrease in public trust when it comes to factual information and in civic processes like elections.

“Frontier AI can be misused to deliberately spread false information to create disruption, persuade people on political issues, or cause other forms of harm or damage,” it says. In regards to the documents, Mr. Sunak added that among the aforementioned risks outlined in the document was also a risk of AI being used by terrorist groups, "to spread fear and disruption on an even greater scale."

He notes that reducing the danger of AI causing the extinction of humans should be a "global priority".

However, he stated: "This is not a risk that people need to be losing sleep over right now and I don't want to be alarmist." He said that, on the whole, he was "optimistic" about AI's capacity to improve people's lives.

The disruption AI is already causing in the workplace is a threat that many will be far more familiar with.

Mr. Sunak emphasized how effectively AI technologies do administrative duties that are typically performed by an employee manually, such as drafting contracts and assisting in decision-making.

He added that technology has always changed how people generate money and that education is the best way to prepare individuals for the shifting market. For example, automation has already altered the nature of employment in factories and warehouses, but it has not completely eliminated human involvement.

The prime minister encouraged people to see artificial intelligence as a "co-pilot" in the day-to-day operations of the workplace, saying it was oversimplified to suggest the technology will "take people's jobs".  

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Technology
AI AI Risks Artificial Intelligence data security Generative AI legal challenges Legal Implications Legal risks Technology

What are the Legal Implications and Risks of Generative AI?


In the ever-evolving AI landscape, dealing with the changing regulations and securing data privacy has become a new challenge. With more efficient human capabilities, AI must not replace humans, especially in a world where its standards are still developing globally. 

There are certain risks that the unchecked generative AI possesses with the overabundant information it may hold. Companies run the risk of disclosing their valuable assets when they feed private, sensitive data into open AI models. Some businesses choose to localize AI models on their systems and train them using their confidential data in order to reduce this danger. However, for best outcomes, such a strategy necessitates a well-organized data architecture.

Risks of Unchecked Generative AI

The appealing elements of generative AI and Large Language Models (LLMs) are their capabilities to compile information to produce fresh ideas, but these skills also carry inherent risks. If not carefully handled, gen AI can unintentionally result in issues like: 

Personal Data Security 

AI systems must handle personal data with the utmost care, especially sensitive or special category personal data. Concerns about unintentional data leaks that could lead to data privacy violations are raised by the growing integration of marketing and consumer data into LLMs.

Contractual Violations 

It is occasionally illegal to use consumer data in AI systems, which has negative legal repercussions. As companies adopt AI, they must carefully negotiate this treacherous terrain to ensure they uphold contractual commitments.

Customer Transparency and Disclosure 

The goals of current and potential future AI regulations focus on a transparent and lucid disclosure of AI technology. For instance, the business must disclose whether a person or an AI is handling a customer's engagement with a chatbot on a support website. Maintaining trust and upholding ethical standards depend on adherence to such restrictions.

Legal Challenges and Risks for Businesses 

Recent legal actions against eminent AI companies highlight the significance of handling data responsibly. The importance of strict data governance and transparency is highlighted by these lawsuits, which include class action cases involving copyright infringement, consumer protection, and data protection issues. They also suggest possible conditions for exposing the origins of AI training data.

Since their use of copyrighted data to build and train their models, AI giants have been the main targets of various lawsuits. Allegations of copyright infringement, consumer protection violations, and data protection legislation violations are made in recent class action lawsuits filed in the Northern District of California, including one filed on behalf of authors and another on behalf of victim users. These submissions emphasize the value of treating data responsibly and could indicate that in the future it will be necessary to identify the sources of training data.

Moreover, businesses possess serious risks when they significantly rely on AI models, not just AI developers like OpenAI. The case of how many of the apps implement improper AI model training may taint entire products. The parent business Everalbum was forced to destroy improperly gathered data and AI models after the Federal Trade Commission (FTC) accused Everalbum of misleading consumers about the use of face recognition technology and data retention. This forced Everalbum to cease in 2020.

How to Mitigate AI Risks? 

Despite the legal challenges, CEOs are under pressure to adopt generative AI if they wish to increase their business’ productivity. Businesses can create best practices and get ready for new requirements by using the frameworks and legislation currently in place. AI systems are covered by provisions in existing data protection regulations, such as those requiring transparency, notice, and the protection of individual privacy rights. Some of these best practices involve:

  • Transparency and Documentation: Businesses are recommended to clearly mention the AI usage, and document AI logic, applications and potential impacts on the data subjects. Also, businesses must keep a record of data transactions and detailed logs of confidential information in order to maintain proper governance and data security.
  • Localizing AI Models: By ensuring that models are trained on pertinent, organization-specific information, internal localization and training with private data can lower data security risks and boost efficiency.
  • Discovering and Connecting: Companies must utilize generative AI to unveil new perspectives and create unexpected connections across different departments and information silos.
  • Preserving Human Element: Gen AI should improve human performance rather than completely replace it. To reduce model biases and data inaccuracies, human monitoring, critical decision review, and content verification of AI-created information are essential.

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