Explainable AI

Explainability Matters Now More Than Ever.

When AI goes rogue, trust evaporates. CISOs and AI leaders know the risks: opaque decisions, unpredictable outputs, and the fear of AI systems making critical choices no one can explain. That’s not just a technical issue—it’s a business liability.

At Aiceberg, we believe enterprises deserve more than a black box. You need clarity. You need control. You need confidence.

Trust What You Can Trace. 

Trust What You Can Trace. 

Benefits of Explainable AI

Aiceberg is the only enterprise platform built to power safe, explainable AI. We act as the guardian agent—watching over every AI interaction to ensure it’s secure, aligned, and understandable.

Full Auditability

Every action is logged, traceable, and reviewable end-to-end. Enterprises get complete audit trails to meet governance, oversight, and compliance requirements with confidence.

Deterministic Decisions

Aiceberg never hallucinates. Our purpose-built, non-generative models produce consistent, deterministic results so you can trust the safeguards protecting your business.

Independent Reliability

Aiceberg doesn’t rely on LLMs to evaluate LLM traffic. Even if ChatGPT or other providers go down, your protection layer stays fully operational — ensuring uninterrupted safety and control.

Rapid Patching

When something needs fixing, you don’t retrain an LLM — you simply add samples. Aiceberg’s purpose-built, non-generative models let you patch behaviors in a day, not weeks. No fine-tuning cycles. No waiting on vendors. Just fast, predictable improvements your team controls.

Compliance Transparency

Easily show regulators and auditors exactly how security decisions are made. Trace reasoning for every Ai-driven decision. It builds trust with stakeholders by letting you clearly explain how and why security actions are taken.

Customization

Feed Aiceberg your own data — policies, logs, examples, or domain-specific cases — and we can build entirely new risk categories and detection rules tailored to your business. No data science team required. No model training cycles. Just fast, bespoke safeguards.

Never monitor a black box with a black box.

Explore the power of the Trace Function.

These images show the training data from our models.

Semantic meaning is how close two pieces of text are in what they’re actually saying—not just the words they use, but the idea, purpose, and the way the sentences are built.

This means Aiceberg looks past the exact words and figures out which examples share the same idea, purpose, or meaning — even if they’re written differently.

These samples come from Aiceberg’s curated training set and represent real past examples the system has already seen and labeled. Think of them as the company your prompt keeps—Aiceberg looks at the “friends” of the input to understand what kind of behavior it resembles.

Scores for distance and relevance of the selected samples are also assigned. A low distance score and high relevance means the inbound is very similar to that sample.

Each sample has a label that describes what kind of behavior it represents, assigned during training by Aiceberg. Now that you have identified the risks with contextual labels and scores that provide confidence in action, Aiceberg can respond to block, redact, alert or execute to act as your Guardian Agent.

If the models lack relevant samples in the training data to apply accurate labels and scores, we do not need to retrain the model. It is a simple update to the training data set.

https://www.youtube.com/watch?v=6oTurM7gESE