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November 16, 2023by Cybersixgill

2024 Predictions: AI Becomes More Accessible as Cybersecurity Vendors Address Data Reliability, Diversity, and Privacy

Cybersixgill published our 2024 predictions, Cybersecurity in 2024: Predicting the Next Generation of Threats and Strategies, that we believe will contribute to the next wave of business transformation. From AI’s impact on both defenders and malicious actors to tighter regulations and cybersecurity mandates to cyber threat intelligence coming of age, the trends we anticipate to take hold in the coming year will test organizations’ approach to risk and resilience. In the coming weeks, we will delve into each prediction and its role in pushing companies to adopt more proactive and adaptive cybersecurity strategies.

CSG-Prediction ThumbnailArtificial intelligence (AI) is not new, but with the launch of ChatGPT, AI has dominated the headlines. The technology is rapidly advancing and becoming more readily available. As the generative AI race takes off with technology companies like Google, Microsoft, and Amazon releasing their own products, the implications of AI are wide-ranging and have a profound impact on people’s lives and across industries. Knowing this, technology companies, cybersecurity vendors, and government leaders need to be mindful of the risks and benefits of AI and data privacy concerns.

As shared in Cybersecurity in 2024: Predicting the Next Generation of Threats and Strategies, Cybersixgill predicts that the breadth and reliability of data will significantly improve in 2024. AI vendors will advance the richness and fidelity of results by developing the model’s core technology and expanding the types of data on which AI models are trained. AI models will become highly sophisticated and handle diverse, hard-to-decipher data sets, such as those found on the dark web and within “hidden” data forms. Additionally, as governments start to develop AI guidelines and regulations, Cybersixgill believes many companies will form their own policies and restrictions as they wait for regulatory legislation to be enacted.

Governments worldwide are just now starting to understand the urgent need to make AI more transparent and safer to use. Most recently, the UK hosted the AI Safety Summit attended by world leaders representing 28 countries. The summit’s focus was a necessary first step in developing best practices to understand, predict, and manage AI-associated risks – from how it is developed to the data on which it is trained. In the United States, the Biden administration released the “AI Bill of Rights” in 2022 to address potential societal harm and outline suggested guidelines for the responsible design and use of AI, including protections for individual personal data. This act has since been followed by Senate and Congressional hearings as the federal government works toward AI safeguards and possible regulations.

Not surprisingly, the common themes seen in AI governance proposals include algorithmic transparency and accountability. AI relies on large sets of data to train its algorithms and improve performance, and this data can contain sensitive information, such as health records, financial information, and social security numbers. All this raises questions about data privacy – how is the data being used, who has access to it, and what if private information is revealed in the AI’s output?

In the cybersecurity industry, many new and exciting potential use cases exist for AI. AI-enabled technologies can help defenders keep pace with the evolving threat landscape by using machine learning algorithms trained on new data to detect and respond to emerging threats quickly. 

One of Cybersixgill’s priorities is protecting our customers’ sensitive data. With the launch of Cybersixtill IQ, our generative AI threat intelligence tool, we have taken the lead in data protection with our framework that other companies can easily follow:

  • Minimize Data Transfer to ensure that only the most essential, non-sensitive information is shared.

  • Mask Sensitive Data to preserve the data structure for analysis while securing sensitive information.

  • Send Metadata Only to exclude the actual content while sharing pertinent details about it. 

  • Use of Differential Privacy to publicly share information about a dataset through descriptions of group patterns while withholding and protecting individual-specific information.

  • Local Processing to limit the data transferred over the public internet. 

  • Develop Proprietary Machine Learning Models trained on our sensitive data on our secure servers to ensure that we maintain control of the data and insights.

Cybersixgill IQ offers complete assurance that users’ data is secure and protected. Read more to learn about the important steps we’re taking.

Want to learn more about Cybersixgill’s insights and predictions for 2024 to keep your assets and stakeholders safe? Download Cybersecurity in 2024: Predicting the Next Generation of Threats and Strategies.

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