Artificial intelligence (AI) is intertwined with almost every aspect of our daily lives, from personalized advice to critical decision-making. Given that AI will continue to move forward, AI-related threats will also become more complex. The next step in promoting the overall security culture is to enhance the interpretability of AI as businesses develop defenses that support AI in response to growing complexity.
Although these systems provide impressive functionality, they often act as “black boxes” – producing results without a clear understanding of how the model draws conclusions. The issue of AI systems making misstatements or taking false actions can lead to significant problems and potential business disruptions. When companies make mistakes due to AI, their customers and consumers need explanations, and soon after, the solution.
But what should be blamed? Usually, poor data are used for training. For example, most public Genai technologies are trained on the data available on the Internet, which are often not verified and inaccurate. Although AI can produce fast responses, the accuracy of these responses depends on the quality of the data trained.
AI errors can occur in a variety of situations, including script generation with wrong commands and wrong security decisions, or avoiding employees from working in business systems due to false allegations from AI systems. All of this has the potential to cause a large number of business disruptions. This is just one of the many reasons why ensuring transparency is key to building trust in AI systems.
Build trust
We exist in a culture where we instill trust in various sources and information. But, at the same time, we require increasing proof and verification, requiring constant verification of news, information and claims. When it comes to AI, we will trust systems that have the potential to be inaccurate. More importantly, without any transparency, it is impossible to know the basis for making a decision. What if your network AI system shuts down the machine but it makes the mistake of explaining the flag? Without a deeper understanding of what information causes the system to make a decision, it is impossible to know whether it has made the right decision.
Despite the frustrating interference to the business, one of the most important issues with AI use is data privacy. AI systems (such as ChatGpt) are machine learning models that can extract answers from the data it receives. Therefore, if a user or developer accidentally provides sensitive information, the machine learning model can use this data to respond to other users revealing confidential information. These mistakes have the potential to seriously undermine the efficiency, profitability and most importantly customer trust. AI systems are designed to improve efficiency and ease processes, but with constant verification, organizations not only waste time due to inability to trust outputs, but also open the door to potential vulnerability.
The training team is responsible for the use of AI
To protect organizations from the potential risks of AI use, IT professionals have the important responsibility to adequately train colleagues to ensure that AI is used responsibly. By doing so, they help protect organizations from cyber attacks that threaten their survivability and profitability.
However, IT leaders need to be consistent internally before training their teams to determine that the AI system is suitable for their organization. Being anxious will only backfire in the future, so start to focus on the needs of the organization. Make sure you choose standards and systems that align with your organization’s current technology stack and company goals, and that your AI systems meet the same security standards as any other vendor of your choice.
Once the system is selected, IT professionals can start to get their teams in contact with these systems to ensure success. First, use AI for small tasks and see where it performs well, where it performs poorly, and then understand the potential dangers or validation of the need for application. Then introduce solutions using AI to enhance the use of work and self-serve yourself at a faster speed, including simple “how” questions. From there, you can teach how to perform verification. This is valuable because we will start to see more work that will become the work that puts boundary conditions and validation together, even in the work of using AI to assist in writing software.
In addition to these feasible steps to train team members, discussions must be conducted and encouraged. Encourage open, data-driven, conversations about how AI meets user needs – is it able to solve problems accurately and faster, are we increasing productivity for companies and end users, and will our customers increase NPS scores because of these AI-driven tools? Be clear about the return on investment (ROI) and keep ahead and center. Clear communication will make people aware of the development of the person in charge and as team members gain a better understanding of how AI systems work, they are more likely to use them responsibly.
How to achieve transparency in AI
While training teams and awareness is important, it is crucial to achieve transparency in AI to have more context around the data to train models to ensure only quality data is used. Hopefully, there will be a way to understand the system’s reasons so that we can fully trust it. But before that we need systems that can use verification and guardrails and prove that they comply with them.
although Full transparency Will be Inevitably take time arrive achieve, this Quickly Grow of AI and It is usage Production it Necessary arrive Work Quickly. As the complexity of AI models continues to increase, they have the ability to have a big impact on humans, but the consequences of errors will also grow. As a result, it is very valuable to understand how these systems make decisions and need to be kept effective and trustworthy. By focusing on transparent AI systems, we can ensure that the technology is useful while maintaining impartial, ethical, efficient and accurate.
