GenAI is the most disruptive technology that has reached society from the Internet. Two years after the release of the most popular large language model, ChatGPT, GenAI tools have fundamentally and forever changed the way we consume information, create content, and interpret data.
Since then, the breakneck speed at which AI tools have emerged and evolved has meant that many companies have found themselves on the defensive when it comes to the regulation, management and governance of GenAI.
This environment has allowed ‘shadow AI’ to run rampant. According to Microsoft, 78% of knowledge workers regularly use their own AI tools to complete their work, but a whopping 52% do not disclose this to employers. As a result, businesses are exposed to a wide range of risks, including data breaches, compliance breaches, and security threats.
Addressing these challenges requires a multi-faceted approach, including strong governance, clear communication, and versatile tracking and management of AI tools, all without compromising staff freedom and flexibility.
General Director of Kolekti.
Trust is paramount and goes both ways.
Employees will use GenAI tools whether their employer requires it or not. In fact, blanket bans or strict restrictions on how it should be used are likely to only exacerbate the challenge of ‘shadow AI’. A recent study even showed that 46% of employees would refuse to give up AI tools even if they were banned.
GenAI is an incredibly accessible technology that has the power to significantly improve efficiency and close skills gaps. These transformative tools are within reach of time-pressed employees and employers cannot, without reasonable justification, tell them they cannot use them.
Therefore, the first step for employers to strike the right balance between efficiency and authenticity is to lay the blueprint for how GenAI can and should be used in a business environment.
Therefore, comprehensive training is essential to ensure employees know how to use AI tools safely and ethically.
This goes beyond technical knowledge: it also includes educating staff about the potential risks associated with AI tools, such as privacy concerns, intellectual property issues, and compliance with regulations such as GDPR.
Clearly explaining these risks will go a long way toward helping staff accept restrictions that may initially seem too harsh.
Describe clear use cases
Defining clear use cases for AI within a given organization is also extremely important, not only telling employees how they can’t use AI, but also how they can use it. In fact, a recent study found that a fifth of staff do not currently use AI because they do not know how.
Therefore, with proper training, awareness, and understanding of how AI tools can be used, you can avoid unnecessary experimentation that can expose your organization to risks while reaping the efficiency rewards that naturally come. with AI.
Of course, clear guidelines must be established on which AI tools are acceptable for use. This can differ across departments and workflows, so it’s important for organizations to take a flexible approach to AI governance.
Once use cases are defined, it is essential to accurately measure AI performance. This includes establishing benchmarks for how AI tools integrate into daily workflow, tracking productivity improvements, and ensuring alignment with business objectives. By establishing metrics to monitor success, companies can better track the adoption of AI tools, ensuring that they are not only used effectively but that their use aligns with the organization’s goals.
Address BYO-AI
One of the main reasons why shadow AI is becoming more serious is that employees can bypass IT departments and implement their own solutions through unauthorized AI tools. The decentralized and plug-and-play nature of many AI platforms allows employees to easily integrate AI into their daily work routines, leading to a proliferation of parallel tools that may not comply with corporate policies or standards. security.
The solution to this problem is through versatile API management. By implementing robust API management procedures, organizations can effectively manage how internal and external AI tools are integrated into their systems.
From a security perspective, API management allows companies to regulate data access, monitor interactions between systems, and ensure that AI applications only interact with appropriate data sets in a controlled and secure manner.
However, it is important not to cross the line of workplace surveillance by monitoring specific inputs and outputs of company-approved tools. This is likely to force AI users back into the shadows.
A good middle ground is to set up sensitive alerts to prevent accidental leaks of sensitive data. For example, AI tools can be configured to detect when AI models inappropriately input or process personal data, financial details, or other proprietary information. Real-time alerts provide an additional layer of protection, ensuring breaches are identified and mitigated before they become full-blown security incidents.
A well-executed API strategy allows you to give employees the freedom to use GenAI tools productively, while protecting source data and ensuring that AI use complies with internal governance policies. This balance can drive innovation and productivity without compromising security or control.
Strike the right balance
By establishing strong governance with defined use cases, leveraging versatile API management for seamless integration, and continually monitoring AI usage for compliance and security risks, organizations can strike the right balance between productivity and protection. . This approach will allow companies to harness the power of AI while minimizing the risks of “shadow AI”, ensuring that GenAI is used in a safe, efficient and compliant manner, while allowing them to unlock a crucial value and return on investment.
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