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When employees are using AI tools without approval from management or the IT department it’s called shadow AI. There are a lot of risks associated with the use of personal accounts on public AI platforms such as security, compliance, data privacy breaches and data leaks. Shadow AI comprises of public AI tools, third-party AI plug-ins and agents, or any SaaS applications with embedded AI features.

When your AI hallucinates, you’re getting answers that sound plausible but aren’t correct. AI hallucination is mainly caused by insufficient or poor quality training data, a flawed logic or incorrect assumptions coded into the algorithms. Think of searching for historical facts that might sound true, but aren’t.

AI bias is a systematic error. When the training data contains prejudices of any kind, it delivers errors in the AI’s decision making process which shows bias against specific demographics or unfairly disadvantages certain groups.

A workflow describes different steps in a process. With AI, these workflows can be intelligently automated in a way that the AI instance recognises what needs to be done and executes without a human pressing a button.

AI ingest describes a crucial process in building an AI model from scrath. You need to collect, transform and load raw data from various sources. Usually, the data feed comprises of both structured and unstructured data. The second important part of the AI ingest process is to prepare the data by cleaning, validating, and structuring it.

AI inference puts a trained AI model into action by making decisions, classifications or predictions based on new data it has never seen before. In this stage, AI starts to deliver value by using learned patterns to generate output based on new and real-world input. Think of an AI model trained on thousands of images of dogs. When it can now identify a dog in a brand new photograph, this identification is the inference.

RAG stands for Retrieval Augmented Generation and is crucial for providing correct and up-to-date information and grounding responses in facts which then reduces AI hallucinations. A RAG pipeline enhances an LLM’s ability to generate accurate and relevant responses by connecting it to external knowledge sources. The process begins with taking external data, splitting it into smaller chunks, and converting these chunks into numerical representations called ’embeddings’. These embeddings are then stored in a vector database. The original user query is then combined with the retrieved text chunks to form an augmented prompt which allows the LLM to produce a more specific and relevant answer.

Private AI ensures data privacy and security for the use of AI within your business. Unlike local AI the data is kept within your controlled infrastructure, but not necessarily on a single local machine. With that said, private AI can be deployed locally, in a private cloud or a hybrid environment.

When you’re keeping AI operations on a local device or network, you’re running a local AI model. All datadets are processed and stored entirely on your own hardware platform.

First of all we’re using open-source models that can be audited and fine-tuned so you can always monitor biases in output and take appropriate action. Since data remains private, you are in full control of training datasets which is important to avoid perpetuating external biases. Broad AI ethics are important to us we we’re promoting transparency and it’s in your hands to AI bias.

Traditional automation follows fixed rules, like the good old “if then” statement. AI agents, on the other hand, react dynamically, learn from experience, recognise patterns in data and can make complex decisions. This makes AI agents significantly more flexible and versatile.

We keep all data processing strictly internal, preventing any leakage of PII such as names, financial details, health records, or credentials. While it doesn’t include built-in redaction tools like those from specialised providers (e.g., Private AI, which focuses on identifying and anonymizing PII across languages and entities), users can integrate such capabilities or apply custom preprocessing to ensure compliance. This approach aligns with heightened AI privacy risks by prioritising data governance and encryption.