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    Home > Finance > Britain to work with Microsoft to build deepfake detection system
    Finance

    Britain to work with Microsoft to build deepfake detection system

    Published by Global Banking & Finance Review®

    Posted on February 5, 2026

    2 min read

    Last updated: February 5, 2026

    Britain to work with Microsoft to build deepfake detection system - Finance news and analysis from Global Banking & Finance Review
    Tags:innovationtechnologycybersecurity

    Quick Summary

    Britain partners with Microsoft to develop a deepfake detection system, aiming to set standards for AI-generated content as concerns rise globally.

    Table of Contents

    • UK's Initiative on Deepfake Detection
    • Concerns Over Deepfake Technology
    • Framework for Detection Standards
    • Global Response to Deepfake Challenges

    UK Collaborates with Microsoft to Develop Deepfake Detection System

    UK's Initiative on Deepfake Detection

    LONDON, Feb 5 (Reuters) - Britain will work with Microsoft, academics and experts to develop a system to spot deepfake material online, the government said on Thursday, as it moves to set standards for tackling harmful and deceptive AI-generated content.

    Concerns Over Deepfake Technology

    While manipulated material has circulated online for decades, the rapid adoption of generative AI chatbots - made possible through the launch of ChatGPT and others - has amplified concerns about the scale and realism of deepfakes.

    Framework for Detection Standards

    Britain, which recently criminalised the creation of non-consensual intimate images, said it was working on a deepfake detection evaluation framework to set consistent standards for assessing detection tools and technologies.

    Global Response to Deepfake Challenges

    "Deepfakes are being weaponised by criminals to defraud the public, exploit women and girls, and undermine trust in what we see and hear," technology minister Liz Kendall said in a statement.

    GOVERNMENTS SPURRED INTO ACTION BY NON-CONSENSUAL IMAGES

    The framework will evaluate how technology can be used to assess, understand and detect harmful deepfake materials, regardless of its source, the government said, by testing deepfake detection technologies against real-world threats like sexual abuse, fraud and impersonation.

    That would help the government and law enforcement obtain better knowledge on where gaps in detection remain, it said, adding that the framework would be used to set clear expectations for industries on deepfake detection standards.

    An estimated 8 million deepfakes were shared in 2025, up from 500,000 in 2023, according to government figures.

    Governments and regulators worldwide, who are struggling to keep pace with the rapid evolution of AI technology, were spurred into action this year, as Elon Musk's Grok chatbot was found to generate non-consensual sexualised images of people, including children.

    The British communications watchdog and privacy regulator are carrying out parallel investigations into Grok.

    (Reporting by Muvija M; Editing by Joe Bavier)

    Key Takeaways

    • •Britain collaborates with Microsoft to develop a deepfake detection system.
    • •The initiative aims to set standards for AI-generated content detection.
    • •Concerns rise with the spread of generative AI chatbots.
    • •The UK government is working on a deepfake detection evaluation framework.
    • •Global response to deepfake challenges is increasing.

    Frequently Asked Questions about Britain to work with Microsoft to build deepfake detection system

    1What is deepfake technology?

    Deepfake technology uses artificial intelligence to create realistic-looking fake videos or audio recordings, often manipulating the appearance or voice of individuals.

    2What is cybersecurity?

    Cybersecurity refers to the practices and technologies designed to protect computers, networks, and data from unauthorized access, attacks, or damage.

    3What is a detection framework?

    A detection framework is a structured approach used to evaluate and assess the effectiveness of tools and technologies designed to identify specific threats or anomalies.

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