Persona control is the ability to guide or manage how an artificial intelligence system presents itself when interacting with users. This means setting specific characteristics, behaviours or tones for the AI, so it matches the intended audience or task. By adjusting these traits, businesses and developers can ensure the AI’s responses feel more consistent and…
Output guards are mechanisms or rules that check and control what information or data is allowed to be sent out from a system. They work by reviewing the output before it leaves, ensuring it meets certain safety, privacy, or correctness standards. These are important for preventing mistakes, leaks, or harmful content from reaching users or…
Intent resolution is the process of figuring out what a user wants to do when they give a command or make a request, especially in software and digital assistants. It takes the input, such as a spoken phrase or typed command, and matches it to a specific action or outcome. This process often involves analysing…
Response filters are tools or processes that modify or manage the information sent back by a system after a request is made. They can check, change, or enhance responses before they reach the user or another system. This helps ensure that the output is correct, safe, and meets certain standards or requirements.
Prompt sanitisation is the process of checking and cleaning user input before it is sent to an AI system or language model. This step helps to remove harmful, inappropriate or malicious content, such as offensive language, private information or code that could be used for attacks. It ensures that prompts are safe, appropriate and do…
AI-powered compliance uses artificial intelligence tools to help organisations meet legal and regulatory requirements. These systems can automatically monitor, analyse, and report on company data to ensure rules are being followed. By automating routine checks and flagging potential problems, AI makes it easier and faster to stay compliant.
Neural robustness frameworks are systems and tools designed to make artificial neural networks more reliable when facing unexpected or challenging situations. They help ensure that these networks continue to perform well even if the data they encounter is noisy, incomplete or intentionally manipulated. These frameworks often include methods for testing, defending, and improving the resilience…
Model robustness metrics are measurements used to check how well a machine learning model performs when faced with unexpected or challenging situations. These situations might include noisy data, small changes in input, or attempts to trick the model. Robustness metrics help developers understand if their models can be trusted outside of perfect test conditions. They…
AI-driven compliance monitoring uses artificial intelligence to help organisations automatically track and ensure that they are following laws, rules, and industry standards. It scans large amounts of data, such as emails, transactions, and documents, to spot potential risks or violations. This approach saves time, reduces human error, and helps companies respond quickly to compliance issues.
AI-driven risk management uses artificial intelligence to help identify, assess, and respond to potential problems or threats. By analysing large amounts of data, AI can spot patterns and alert people to risks that might otherwise go unnoticed. This approach can make decision-making faster and more accurate, helping organisations reduce losses and improve safety.