AI has become almost ubiquitous in software development, as a GitHub survey shows, 92 per cent of developers in the United States use artificial intelligence as part of their everyday coding. This has led many individuals to participate in what is termed “shadow AI,” which involves leveraging the technology without the knowledge or approval of their organization’s Information Technology department and/or Chief Information Security Officer (CISO).
This has increased their productivity.
In light of this, it should not come as a surprise to learn that motivated employees will seek out the technology that can maximize their value potential as well as minimize repetitive tasks that interfere with more creative, challenging endeavours. It is not uncommon for companies to be curious about new technologies, especially those that can be used to make work easier and more efficient, such as artificial intelligence (AI) and automation tools.
Despite the increasing amount of ingenuity, some companies remain reluctant to adopt technology at their first, or even second, glances. Nevertheless, resisting change does not necessarily mean employees will stop secretly using AI in a non-technical way, especially since tools such as Microsoft Copilot, ChatGPT, and Claude make these technologies more accessible to non-technical employees.
Known as shadow AI, shadow AI is a growing phenomenon that has gained popularity across many different sectors.
There is a concept known as shadow AI, which is the use of artificial intelligence tools or systems without the official approval or oversight of the organization's information technology or security department. These tools are often adopted to solve immediate problems or boost efficiency within an organization.
If these tools are not properly governed, they can lead to data breaches, legal violations, or regulatory non-compliance, which could pose significant risks to businesses.
When Shadow AI is not properly managed, it can introduce vulnerabilities into users' infrastructure that can lead to unauthorized access to sensitive data. In a world where artificial intelligence is becoming increasingly ubiquitous, organizations should take proactive measures to make sure their operations are protected.
Shadow generative AI poses specific and substantial risks to an organization's integrity and security, and poses significant threats to both of them.
A non-regulated use of artificial intelligence can lead to decisions and actions that could undermine regulatory and corporate compliance. Particularly in industries with very strict data handling protocols, such as finance and healthcare, where strict data handling protocols are essential.
As a result of the bias inherent in the training data, generative AI models can perpetuate these biases, generate outputs that breach copyrights, or generate code that violates licensing agreements.
The untested code may cause the software to become unstable or error-prone, which can increase maintenance costs and cause operational disruptions. In addition, such code may contain undetected malicious elements, which increases the risk of data breach and system downtime, as well.
It is important to recognize that the mismanagement of Artificial Intelligence interactions in customer-facing applications can result in regulatory non-compliance, reputational damage, as well as ethical concerns, particularly when the outputs adversely impact the customer experience.
Consequently, organization leaders must ensure that their organizations are protected from unintended and adverse consequences when utilizing generative AI by implementing robust governance measures to mitigate these risks.
In recent years, AI technology, including generative and conversational AI, has seen incredible growth in popularity, leading to widespread grassroots adoption of these technologies.
The accessibility of consumer-facing AI tools, which require little to no technical expertise, combined with a lack of formal AI governance, has enabled employees to utilize unvetted AI solutions, The 2025 CX Trends Report highlights a 250% year-over-year increase in shadow AI usage in some industries, exposing organizations to heightened risks related to data security, compliance, and business ethics.
There are many reasons why employees turn to shadow AI for personal or team productivity enhancement because they are dissatisfied with their existing tools, because of the ease of access, and because they want to enhance the ability to accomplish specific tasks.
In the future, this gap will grow as CX Traditionalists delay the development of AI solutions due to limitations in budget, a lack of knowledge, or an inability to get internal support from their teams.
As a result, CX Trendsetters are taking steps to address this challenge by adopting approved artificial intelligence solutions like AI agents and customer experience automation, as well as ensuring the appropriate oversight and governance are in place.
Identifying AI Implementations: CISOs and security teams, must determine who will be introducing AI throughout the software development lifecycle (SDLC), assess their security expertise, and evaluate the steps taken to minimize risks associated with AI deployment.
In training programs, it is important to raise awareness among developers of the importance and potential of AI-assisted code as well as develop their skills to address these vulnerabilities.
To identify vulnerable phases of the software development life cycle, the security team needs to analyze each phase of the SDLC and identify if any are vulnerable to unauthorized uses of AI.
Fostering a Security-First Culture:
By promoting a proactive protection mindset, organizations can reduce the need for reactive fixes by emphasizing the importance of securing their systems from the onset, thereby saving time and money. In addition to encouraging developers to prioritize safety and transparency over convenience, a robust security-first culture, backed by regular training, encourages a commitment to security.
CISOs are responsible for identifying and managing risks associated with new tools and respecting decisions made based on thorough evaluations. This approach builds trust, ensures tools are properly vetted before deployment, and safeguards the company's reputation.
Incentivizing Success:
There is great value in having developers who contribute to bringing AI usage into compliance with their organizations.
For this reason, these individuals should be promoted, challenged, and given measurable benchmarks to demonstrate their security skills and practices. As organizations reward these efforts, they create a culture in which AI deployment is considered a critical, marketable skill that can be acquired and maintained.
If these strategies are implemented effectively, a CISO and development teams can collaborate to manage AI risks the right way, ensuring faster, safer, and more effective software production while avoiding the pitfalls caused by shadow AI.
As an alternative to setting up sensitive alerts to make sure that confidential data isn't accidentally leaked, it is also possible to set up tools using artificial intelligence, for example, to help detect when a model of artificial intelligence incorrectly inputs or processes personal data, financial information, or other proprietary information.
It is possible to identify and mitigate security breaches in real-time by providing real-time alerts in real-time, and by enabling management to reduce these breaches before they escalate into a full-blown security incident, adding a layer of security protection, in this way.
When an API strategy is executed well, it is possible to give employees the freedom to use GenAI tools productively while safeguarding the company's data, ensuring that AI usage is aligned with internal policies, and protecting the company from fraud. To increase innovation and productivity, one must strike a balance between securing control and ensuring that security is not compromised.
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