BondingAI's new AI Agent for Anomaly Detection & Cybersecurity
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BondingAI's new AI Agent for Anomaly Detection & Cybersecurity
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With two large litigation firms using it and working on implementation with one of the largest corporate compliance companies (customized version specific to their needs).
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With two large litigation firms using it and working on implementation with one of the largest corporate compliance companies (customized version specific to their needs).
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The input data comes from an Excel repository, automatically processed by an AI agent part of our BondingAI enterprise solutions. It comes with insights generation via automated SQL queries and pattern detection. In other contexts, the data may come from a PDF repository, the Internet, databases, or a combination of all.
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The input data comes from an Excel repository, automatically processed by an AI agent part of our BondingAI enterprise solutions. It comes with insights generation via automated SQL queries and pattern detection. In other contexts, the data may come from a PDF repository, the Internet, databases, or a combination of all.
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In this article, I showcase animated data produced by the generic anomaly detection agent. The goal is to illustrate granular spatial fraud patterns as they evolve over time in the video, without having to rely on analysts or statisticians to produce striking insights or to clean the data. Each video frame represents a day, with timestamp in the top left corner. The data comes from two different time periods, showing the sharp contrast in fraud patterns between year 2019 and 2022 as you progress in the video.
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In this article, I showcase animated data produced by the generic anomaly detection agent. The goal is to illustrate granular spatial fraud patterns as they evolve over time in the video, without having to rely on analysts or statisticians to produce striking insights or to clean the data. Each video frame represents a day, with timestamp in the top left corner. The data comes from two different time periods, showing the sharp contrast in fraud patterns between year 2019 and 2022 as you progress in the video.
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Once you start the video, use the cursor at the bottom to move backward or forward in time at a different speed, or to stop on any particular day. Read more here.
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Once you start the video, use the cursor at the bottom to move backward or forward in time at a different speed, or to stop on any particular day. Read more here.
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See also our new book No-Blackbox, Secure, Efficient AI and LLM Solutions. In talks to be published by Wiley, with the following testimonial from a Global Head of AI/ML at JP Morgan Chase: "Your book is great. I have been reading it. It is perfect to be used by regulated industry".
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See also our new book No-Blackbox, Secure, Efficient AI and LLM Solutions. In talks to be published by Wiley, with the following testimonial from a Global Head of AI/ML at JP Morgan Chase: "Your book is great. I have been reading it. It is perfect to be used by regulated industry".
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More details at 
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More details at 
https://www.linkedin.com/pulse/bondingais-new-ai-agent-anomaly-detection-cybersecurity-bonding-ai-kmdwc/
https://www.linkedin.com/pulse/bondingais-new-ai-agent-anomaly-detection-cybersecurity-bonding-ai-kmdwc/
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BondingAI's new AI Agent for Anomaly Detection & Cybersecurity

With two large litigation firms using it and working on implementation with one of the largest corporate compliance companies (customized version specific to their needs).

The input data comes from an Excel repository, automatically processed by an AI agent part of our BondingAI enterprise solutions. It comes with insights generation via automated SQL queries and pattern detection. In other contexts, the data may come from a PDF repository, the Internet, databases, or a combination of all.

In this article, I showcase animated data produced by the generic anomaly detection agent. The goal is to illustrate granular spatial fraud patterns as they evolve over time in the video, without having to rely on analysts or statisticians to produce striking insights or to clean the data. Each video frame represents a day, with timestamp in the top left corner. The data comes from two different time periods, showing the sharp contrast in fraud patterns between year 2019 and 2022 as you progress in the video.

Once you start the video, use the cursor at the bottom to move backward or forward in time at a different speed, or to stop on any particular day. Read more here.

See also our new book No-Blackbox, Secure, Efficient AI and LLM Solutions. In talks to be published by Wiley, with the following testimonial from a Global Head of AI/ML at JP Morgan Chase: "Your book is great. I have been reading it. It is perfect to be used by regulated industry".

More details at https://www.linkedin.com/pulse/bondingais-new-ai-agent-anomaly-detection-cybersecurity-bonding-ai-kmdwc/ 
👀
unmodeled-tyler John6666