In today's digitised world, where artificial intelligence tools are rapidly reshaping the way people work, communicate, and work together, it's important to be aware that a quiet but pressing risk has emerged-that what individuals choose to share with chatbots may not remain entirely private for everyone involved.
A patient can use ChatGPT to receive health advice about an embarrassing health condition, or an employee can upload sensitive corporate documents into Google's Gemini system to generate a summary of them, but the information they disclose will ultimately play a part in the algorithms that power these systems.
It has come to the attention of a lot of experts that AI models, built on large datasets collected from all across the internet, such as blogs and news articles, as well as from social media posts, are often trained without user consent, resulting in not only copyright problems but also significant privacy concerns.
In light of the opaque nature of machine learning processes, experts warn that once data has been ingested into a model's training pool, it will be almost impossible to remove it. In this world we live in, individuals and businesses alike are forced to ask themselves what level of trust we can place in tools that, while extremely powerful, may also expose us to unseen risks.
Considering that we are living in a hybrid age, where artificial intelligence tools such as ChatGPT are rapidly becoming a new frontier for data breaches, this is particularly true in the age of hybrid work. While these platforms offer businesses a number of valuable features, including the ability to draft content and troubleshoot software, they also carry inherent risks.
Experts warn that poor management of them can result in leakage of training data, violations of privacy, and accidental disclosure of sensitive company data.
The latest Fortinet Work From Anywhere study highlights the magnitude of the problem: nearly 62% of organisations have reported experiencing data breaches as a result of switching to remote working.
Analysts believe some of these incidents could have been prevented if employees had stayed on-premises with company-managed devices and applications and had not experienced the same issues.
Nevertheless, security experts claim that the solution is not to return to the office again, but rather to create a robust framework for data loss prevention (DLP) in a decentralised work environment to safeguard the information.
To prevent sensitive information from being lost, stolen, or leaked across networks, storage systems, endpoints, and cloud environments, a robust DLP strategy combines tools, technologies, and best practices.
A successful framework focuses on data at rest, in motion, and in use and ensures that they are continuously monitored and protected.
Experts outline four essential components that a framework must have to succeed:
Make sure the company data is classified and assigned security levels across the network, and that the network is secure.
Maintain strict adherence to compliance when storing, deleting, and retaining user information.
Make sure staff are educated regarding clear policies that prevent accidental sharing of information or unauthorised access to information.
Embrace protection tools that can detect phishing, ransomware, insider threats, and unintentional exposures in order to protect the organisation's data.
It is not enough to use technology alone to protect organisations; it is also essential to have clear policies in place. With DLP implemented correctly, organisations are not only less likely to suffer from leaks, but they are also more likely to comply with industry standards, government regulations, and the like.
The balance between innovation and responsibility in the digital age, particularly in the era of digital transformation, is crucial for businesses that adopt hybrid work and AI-based tools. According to the UK General Data Protection Regulation (UK GDPR), businesses that utilise AI platforms, such as ChatGPT, must adhere to a set of strict obligations designed to protect personal information from unauthorised access.
In terms of data protection, any data that could identify the individual - such as an employee file, customer contact details, or client database - falls within the regulations' scope, and ultimately, business owners are responsible for protecting that data, even when it is handled by third parties.
In order to cope with this scenario, companies will need to carefully evaluate how external platforms process, store, and protect their data.
They often do so through legally binding Data Processing Agreements that specify confidentiality standards, privacy controls, and data deletion requirements for the platforms.
It is equally important to ensure that organisations communicate with individuals when their information is incorporated into artificial intelligence tools and, if necessary, obtain explicit consent from them.
As part of the law, firms are also required to implement “appropriate technical and organisational measures.” These measures include checking whether AI vendors are storing their data overseas, ensuring that it is kept in order to prevent misuse, and determining what safeguards are in place.
Besides the risks of financial penalties or fines that are imposed for failing to comply, there is also the risk of eroding employee and customer trust, which can be more difficult to repair than the financial penalties themselves.
When it comes to ensuring safe data practices in the age of artificial intelligence, businesses are increasingly turning to Data Loss Prevention (DLP) solutions as a way of automating the otherwise unmanageable task of monitoring vast networks of users, devices, and applications, which can be a daunting task.
The state and flow of information have determined the four primary categories of DLP software that have emerged.
Often, DLP tools utilise artificial intelligence and machine learning to identify suspicious traffic within and outside a company's system — whether by downloading, transferring, or through mobile connections — by tracking data movement within and outside a company's systems.
In addition to preventing unauthorised activities at the source, endpoint DLP is also installed directly on users' computers, which monitors memory, cached data, and files being accessed or transferred as they occur.
In general, cloud DLP solutions are intended to safeguard information stored in online environments such as backups, archives, and databases. They are characterised by encryption, scanning, and access controls that are used for securing corporate assets.
While Email DLP is largely responsible for keeping sensitive details from being leaked through internal and external correspondence, it is also designed to prevent these sensitive details from getting shared accidentally, maliciously or through a compromised mailbox as well.
Despite some businesses' concerns about whether Extended Detection and Response (XDR) platforms are adequate, experts think that DLP serves a different purpose than XDR: XDR provides broad threat detection and incident response, while DLP focuses on protecting sensitive data, categorising information, reducing breach risks, and ultimately maintaining company reputations.
A number of major technology companies have adopted varying approaches to dealing with the data their AI chatbots have collected from their users, often raising concerns about transparency and control. Google, for example, maintains conversations with its Gemini chatbot by default for 18 months, but the setting can be modified if users desire.
Although activity tracking can be disabled, these chats remain in storage for at least 72 hours even if they are not reviewed by human moderators in order to refine the system.
However, Google warns users that sharing confidential information is not advisable and that any conversations that have already been flagged for human review cannot be erased.
As part of Meta's artificial intelligence assistant, which can be found on Facebook, WhatsApp, and Instagram, it is trained to understand public posts, photos, captions, and data scraped from around the web. However, the application cannot handle private messages.
There is no doubt that citizens of the European Union and the United Kingdom have the right to object to the use of their information for training under stricter privacy laws. However, those living in countries without such protections, such as the United States, have fewer options than their citizens in other countries. The opt-out process for Meta is quite complicated, and it is available only where it is available; users must submit evidence of their interactions with the chatbot as evidence of the opt-out.
It is worth noting that Microsoft's Copilot does not provide an opt-out mechanism for personal accounts; users are only limited in their ability to delete their interaction history through their account settings, and there is no option to prevent future data retention. These practices demonstrate how AI privacy controls can be patchy, with users' choices often being more influenced by the laws and regulations of their jurisdiction, rather than corporate policy.
The responsibility organisations as they navigate this evolving landscape relates not only to complying with regulations or implementing technical safeguards, but also to cultivating a culture of digital responsibility in their organisations. Employees need to be taught how important it is to understand and respect the value of their information, and how important it is to exercise caution when using AI-powered applications.
By taking proactive measures such as implementing clear guidelines on chatbot usage, conducting regular risk assessments, and ensuring that vendors are compliant with stringent data protection standards, an organisation can significantly reduce the threat exposure they are exposed to.
The businesses that implement a strong governance framework, at the same time, are not only protected but are also able to take advantage of AI's advantages with confidence, enhancing productivity, streamlining workflows, and maintaining competitiveness in an era of data-driven economies. Thus, it is essential to embrace AI responsibly, balancing innovation and vigilance, so that it isn't avoided, but rather embraced responsibly.
A company's use of AI can be transformed from a potential liability to a strategic asset by combining regulatory compliance, advanced DLP solutions, and transparent communication with staff and stakeholders. It is important to remember that trust is currency in a marketplace where security is king, and companies that protect sensitive data will not only prevent costly breaches from occurring but also strengthen their reputation in the long run.
