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Showing posts with label AI governance. Show all posts

Unauthorized Use of AI Tools by Employees Exposes Sensitive Corporate Data


 

Artificial intelligence has rapidly revolutionised the modern workplace, creating both unprecedented opportunities and presenting complex challenges at the same time. Despite the fact that AI was initially conceived to improve productivity, it has quickly evolved into a transformational force that has changed the way employees think, work, and communicate. 

Despite the rapid rise in technology, many organisations are still ill-prepared to deal with the unchecked use of artificial intelligence. With the advent of generative AI, which can produce text, images, videos, and audio in a variety of ways, employees have increasingly adopted it for drafting emails, preparing reports, analysing data, and even creating creative content. 

The ability of advanced language models, which have been trained based on vast datasets, to mimic the language of humans with remarkable fluency can enable workers to perform tasks that once took hours to complete. According to some surveys, a majority of American employees rely on AI tools, often without formal approval or oversight, which are freely accessible with a little more than an email address to use. 

Platforms such as ChatGPT, where all you need is an email address if you wish to use the tool, are inspiring examples of this fast-growing trend. Nonetheless, this widespread use of unregulated artificial intelligence tools raises many concerns about privacy, data protection, and corporate governance—a concern employers must address with clear policies, robust safeguards, and a better understanding of the evolving digital landscape to prevent these concerns from becoming unfounded. 

Cybernews has recently found out that the surge in unapproved AI use in the workplace is a concerning phenomenon. While digital risks are on the rise, a staggering 75 per cent of employees who use so-called “shadow artificial intelligence” tools admit to having shared sensitive or confidential information through them.

Information that could easily compromise their organisations. However, what is more troubling is that the trend is not restricted to junior staff; it is actually a trend led by the leadership at the organisation. With approximately 93 per cent of executives and senior managers admitting to using unauthorised AI tools, it is clear that executives and senior managers are the most frequent users. Management accounts for 73 per cent, followed by professionals who account for 62 per cent. 

In other words, it seems that unauthorised AI tools are not isolated, but rather a systemic problem. In addition to employee records and customer information, internal documents, financial and legal records, and proprietary code, these categories of sensitive information are among the most commonly exposed categories, each of which can lead to serious security breaches each of which has the potential to be a major vulnerability. 

However, despite nearly nine out of ten workers admitting that utilising AI entails significant risks, this continues to happen. It has been found that 64 per cent of respondents recognise the possibility of data leaks as a result of unapproved artificial intelligence tools, and more than half say they will stop using those tools if such a situation occurs. However, proactive measures remain rare in the industry. As a result, there is a growing disconnect between awareness and action in corporate data governance, one that could have profound consequences if not addressed. 

There is also an interesting paradox within corporate hierarchies revealed by the survey: even though senior management is often responsible for setting data governance standards, they are the most frequent infringers on those standards. According to a recent study, 93 per cent of executives and senior managers use unapproved AI tools, outpacing all other job levels by a wide margin.

There is also a significant increase in engagement with unauthorised platforms by managers and team leaders, who are responsible for ensuring compliance and modelling best practices within the organisation. This pattern, researchers suggest, reflects a worrying disconnect between policy enforcement and actual behaviour, one that erodes accountability from the top down. Žilvinas GirÄ—nas, head of product at Nexos.ai, warns that the implications of such unchecked behaviour extend far beyond simple misuse. 

The truth is that it is impossible to determine where sensitive data will end up if it is pasted into unapproved AI tools. "It might be stored, used to train another model, exposed in logs, or even sold to third parties," he explained. It could be possible to slip confidential contracts, customer details, or internal records quietly into external systems without detection through such actions, he added.

A study conducted by IBM underscores the seriousness of this issue by estimating that shadow artificial intelligence can result in an average data breach cost of up to $670,000, an expense that few companies are able to afford. Even so, the Cybernews study found that almost one out of four employers does not have formal policies in place governing artificial intelligence use in the workplace. 

Experts believe that awareness alone will not be enough to prevent these risks from occurring. As Sabeckis noted, “It would be a shame if the only way to stop employees from using unapproved AI tools was through the hard lesson of a data breach. For many companies, even a single breach can be catastrophic. GirÄ—nas echoed this sentiment, emphasising that shadow AI “thrives in silence” when leadership fails to act decisively. 

The speaker warned that employees will continue to rely on whatever tools seem convenient to them if clear guidelines and sanctioned alternatives are not provided, leading to efficiency shortcuts becoming potential security breaches without clear guidelines and sanctioned alternatives. Experts emphasise that organisations must adopt comprehensive internal governance strategies to mitigate the growing risks associated with the use of unregulated artificial intelligence, beyond technical safeguards. 

There are a number of factors that go into establishing a well-structured artificial intelligence framework, including establishing a formal AI policy. This policy should clearly state the acceptable uses for AI, prohibit the unauthorised download of free AI tools, and limit the sharing of personal, proprietary, and confidential information through these platforms. 

Businesses are also advised to revise and update existing IT, network security, and procurement policies in order to keep up with the rapidly changing AI environment. Additionally, proactive employee engagement continues to be a crucial part of addressing AI-related risks. Training programs can provide workers with the information and skills needed to understand potential risks, identify sensitive information, and follow best practices for safe, responsible use of AI. 

Also essential is the development of a robust data classification strategy that enables employees to recognise and handle confidential or sensitive information before interacting with AI systems in a proper manner. 

The implementation of formal authorisation processes for AI tools may also benefit organisations by limiting access to the tools to qualified personnel, along with documentation protocols that document inputs and outputs so that compliance and intellectual property issues can be tracked. Further safeguarding the reputation of your brand can be accomplished by periodic reviews of AI-generated content for bias, accuracy, and appropriateness. 

By continuously monitoring AI tools, including reviewing their evolving terms of service, organisations can ensure ongoing compliance with their company's standards, as well. Finally, it is important to put in place a clearly defined incident response plan, which includes designated points of contact for potential data exposure or misuse. This will help organisations respond more quickly to any AI-related incident. 

Combined, these measures represent a significant step forward in the adoption of structured, responsible artificial intelligence that balances innovation and accountability. Although internal governance is the cornerstone of responsible AI usage, external partnerships and vendor relationships are equally important when it comes to protecting organisational data. 

According to experts, organisation leaders need to be vigilant not just about internal compliance, but also about third-party contracts and data processing agreements. Data privacy, retention, and usage provisions should be explicitly included in any agreement with an external AI provider. These provisions are meant to protect confidential information from being exploited or stored in ways that are outside of the intended use of the information.

Business leaders, particularly CEOs and senior executives, must examine vendor agreements carefully in order to ensure that they are aligned with international data protection frameworks, such as the General Data Protection Regulation and California Consumer Privacy Act (CCPA). In order to improve their overall security posture, organisations can ensure that sensitive data is handled with the same rigour and integrity as their internal privacy standards by incorporating these safeguards into the contract terms. 

In the current state of artificial intelligence, which has been redefining the limits of workplace efficiency, its responsible integration has become an important factor in enhancing organisational trust and resilience as it continues to redefine the boundaries of workplace efficiency. Getting AI to work effectively in business requires not only innovation but also a mature set of governance frameworks that accompany its use. 

Companies that adopt a proactive approach, such as by enforcing clear internal policies, establishing transparency with vendors, and cultivating a culture of accountability, will be able to gain more than simply security. They will also gain credibility with clients and employees, as well as regulators. Although internal governance is the cornerstone of responsible AI usage, external partnerships and vendor relationships are equally important when it comes to protecting organisational data. 

According to experts, organisation leaders need to be vigilant not just about internal compliance, but also about third-party contracts and data processing agreements. Data privacy, retention, and usage provisions should be explicitly included in any agreement with an external AI provider. 

These provisions are meant to protect confidential information from being exploited or stored in ways that are outside of the intended use of the information. Business leaders, particularly CEOs and senior executives, must examine vendor agreements carefully in order to ensure that they are aligned with international data protection frameworks, such as the General Data Protection Regulation and California Consumer Privacy Act (CCPA). 

In order to improve their overall security posture, organisations can ensure that sensitive data is handled with the same rigour and integrity as their internal privacy standards by incorporating these safeguards into the contract terms. In the current state of artificial intelligence, which has been redefining the limits of workplace efficiency, its responsible integration has become an important factor in enhancing organisational trust and resilience as it continues to redefine the boundaries of workplace efficiency. 

Getting AI to work effectively in business requires not only innovation but also a mature set of governance frameworks that accompany its use. Companies that adopt a proactive approach, such as by enforcing clear internal policies, establishing transparency with vendors, and cultivating a culture of accountability, will be able to gain more than simply security. They will also gain credibility with clients and employees, as well as regulators.

In addition to ensuring compliance, responsible AI adoption can improve operational efficiency, increase employee confidence, and strengthen brand loyalty in an increasingly data-conscious market. According to experts, artificial intelligence should not be viewed merely as a risk to be controlled, but as a powerful tool to be harnessed under strong ethical and strategic guidelines. 

It is becoming increasingly apparent that in today's business climate, every prompt, every dataset can potentially create a vulnerability, so organisations that thrive will be those that integrate technological ambition with a disciplined governance framework - trying to transform AI from being a source of uncertainty to being a tool for innovation that is as sustainable and secure as possible.

Racing Ahead with AI, Companies Neglect Governance—Leading to Costly Breaches

 

Organizations are deploying AI at breakneck speed—so rapidly, in fact, that foundational safeguards like governance and access controls are being sidelined. The 2025 IBM Cost of a Data Breach Report, based on data from 600 breached companies, finds that 13% of organizations have suffered breaches involving AI systems, with 97% of those lacking basic AI access controls. IBM refers to this trend as “do‑it‑now AI adoption,” where businesses prioritize quick implementation over security. 

The consequences are stark: systems deployed without oversight are more likely to be breached—and when breaches occur, they’re more costly. One emerging danger is “shadow AI”—the widespread use of AI tools by staff without IT approval. The report reveals that organizations facing breaches linked to shadow AI incurred about $670,000 more in costs than those without such unauthorized use. 

Furthermore, 20% of surveyed organizations reported such breaches, yet only 37% had policies to manage or detect shadow AI. Despite these risks, companies that integrate AI and automation into their security operations are finding significant benefits. On average, such firms reduced breach costs by around $1.9 million and shortened incident response timelines by 80 days. 

IBM’s Vice President of Data Security, Suja Viswesan, emphasized that this mismatch between rapid AI deployment and weak security infrastructure is creating critical vulnerabilities—essentially turning AI into a high-value target for attackers. Cybercriminals are increasingly weaponizing AI as well. A notable 16% of breaches now involve attackers using AI—frequently in phishing or deepfake impersonation campaigns—illustrating that AI is both a risk and a defensive asset. 

On the cost front, global average data breach expenses have decreased slightly, falling to $4.44 million, partly due to faster containment via AI-enhanced response tools. However, U.S. breach costs soared to a record $10.22 million—underscoring how inconsistent security practices can dramatically affect financial outcomes. 

IBM calls for organizations to build governance, compliance, and security into every step of AI adoption—not after deployment. Without policies, oversight, and access controls embedded from the start, the rapid embrace of AI could compromise trust, safety, and financial stability in the long run.

The Need for Unified Data Security, Compliance, and AI Governance

 

Businesses are increasingly dependent on data, yet many continue to rely on outdated security infrastructures and fragmented management approaches. These inefficiencies leave organizations vulnerable to cyber threats, compliance violations, and operational disruptions. Protecting data is no longer just about preventing breaches; it requires a fundamental shift in how security, compliance, and AI governance are integrated into enterprise strategies. A proactive and unified approach is now essential to mitigate evolving risks effectively. 

The rapid advancement of artificial intelligence has introduced new security challenges. AI-powered tools are transforming industries, but they also create vulnerabilities if not properly managed. Many organizations implement AI-driven applications without fully understanding their security implications. AI models require vast amounts of data, including sensitive information, making governance a critical priority. Without robust oversight, these models can inadvertently expose private data, operate without transparency, and pose compliance challenges as new regulations emerge. 

Businesses must ensure that AI security measures evolve in tandem with technological advancements to minimize risks. Regulatory requirements are also becoming increasingly complex. Governments worldwide are enforcing stricter data privacy laws, such as GDPR and CCPA, while also introducing new regulations specific to AI governance. Non-compliance can result in heavy financial penalties, reputational damage, and operational setbacks. Businesses can no longer treat compliance as an afterthought; instead, it must be an integral part of their data security strategy. Organizations must shift from reactive compliance measures to proactive frameworks that align with evolving regulatory expectations. 

Another significant challenge is the growing issue of data sprawl. As businesses store and manage data across multiple cloud environments, SaaS applications, and third-party platforms, maintaining control becomes increasingly difficult. Security teams often lack visibility into where sensitive information resides, making it harder to enforce access controls and protect against cyber threats. Traditional security models that rely on layering additional tools onto existing infrastructures are no longer effective. A centralized, AI-driven approach to security and governance is necessary to address these risks holistically. 

Forward-thinking businesses recognize that managing security, compliance, and AI governance in isolation is inefficient. A unified approach consolidates risk management efforts into a cohesive, scalable framework. By breaking down operational silos, organizations can streamline workflows, improve efficiency through AI-driven automation, and proactively mitigate security threats. Integrating compliance and security within a single system ensures better regulatory adherence while reducing the complexity of data management. 

To stay ahead of emerging threats, organizations must modernize their approach to data security and governance. Investing in AI-driven security solutions enables businesses to automate data classification, detect vulnerabilities, and safeguard sensitive information at scale. Shifting from reactive compliance measures to proactive strategies ensures that regulatory requirements are met without last-minute adjustments. Moving away from fragmented security solutions and adopting a modular, scalable platform allows businesses to reduce risk and maintain resilience in an ever-evolving digital landscape. Those that embrace a forward-thinking, unified strategy will be best positioned for long-term success.

GenAI Presents a Fresh Challenge for SaaS Security Teams

The software industry witnessed a pivotal moment with the introduction of Open AI's ChatGPT in November 2022, sparking a race dubbed the GenAI race. This event spurred SaaS vendors into a frenzy to enhance their tools with generative AI-driven productivity features.

GenAI tools serve a multitude of purposes, simplifying software development for developers, aiding sales teams in crafting emails, assisting marketers in creating low-cost unique content, and facilitating brainstorming sessions for teams and creatives.

Notable recent launches in the GenAI space include Microsoft 365 Copilot, GitHub Copilot, and Salesforce Einstein GPT, all of which are paid enhancements, indicating the eagerness of SaaS providers to capitalize on the GenAI trend. Google is also gearing up to launch its SGE (Search Generative Experience) platform, offering premium AI-generated summaries instead of conventional website listings.

The rapid integration of AI capabilities into SaaS applications suggests that it won't be long before AI becomes a standard feature in such tools.

However, alongside these advancements come new risks and challenges for users. The widespread adoption of GenAI applications in workplaces is raising concerns about exposure to cybersecurity threats.

GenAI operates by training models to generate data similar to the original based on user-provided information. This exposes organizations to risks such as IP leakage, exposure of sensitive customer data, and the potential for cybercriminals to use deepfakes for phishing scams and identity theft.

These concerns, coupled with the need to comply with regulations, have led to a backlash against GenAI applications, especially in industries handling confidential data. Some organizations have even banned the use of GenAI tools altogether.

Despite these bans, organizations struggle to control the use of GenAI applications effectively, as they often enter the workplace without proper oversight or approval.

In response to these challenges, the US government is urging organizations to implement better governance around AI usage. This includes appointing Chief AI Officers to oversee AI technologies and ensure responsible usage.

With the rise of GenAI applications, organizations need to reassess their security measures. Traditional perimeter protection strategies are proving inadequate against modern threats, which target vulnerabilities within organizations.

To regain control and mitigate risks associated with GenAI apps, organizations can adopt advanced zero-trust solutions like SSPM (SaaS Security Posture Management). These solutions provide visibility into AI-enabled apps and assess their security posture to prevent, detect, and respond to threats effectively.