Risk

Explainable AI Risk Scoring

A dynamic risk score for every AI interaction, combining weighted-factor scoring with behavioural anomaly detection — always explainable.

In short

An explainable AI risk score rates each interaction and shows the weighted factors that produced the rating, rather than emitting an opaque number. KavachLab combines deterministic weighted-factor scoring — data sensitivity, destination model, user context, and policy history — with behavioural anomaly detection (UEBA) that flags deviations from a user’s own established baseline. Every score exposes its top contributing factors, so an analyst can validate the reasoning and an auditor can review the basis for any enforcement decision.

What it gives you

Weighted-factor scoring
Deterministic, tunable, and reviewable — you can see and adjust exactly what drives a score.
Behavioural baselines
Anomalies are measured against each user’s own history, not a generic population average.
Top factors, always
Every score ships with the reasons behind it, so no analyst is asked to trust a bare number.
AI-specific threats
Detects prompt injection, jailbreak attempts, and staged data-exfiltration patterns.

Why explainability is a requirement, not a feature

An unexplainable score fails at exactly the moment it matters. Three groups will ask why, and none of them accept a number on its own:

The analyst triaging the alert, who needs to decide in seconds whether it is real.
The employee who was blocked, who will escalate if the reason is not clear and defensible.
The auditor or regulator, who under frameworks like the EU AI Act expects demonstrable, documented reasoning behind automated decisions.

What feeds the score

Scoring is a hybrid, so it captures both the objective sensitivity of an interaction and its behavioural context:

Data sensitivity — the classification and volume of what is being sent.
Destination — whether the model is sanctioned, public, or known to train on input.
Identity and context — role, business unit, device posture, and location.
Behaviour — deviation from the user’s established pattern of AI usage.
History — prior policy violations and their outcomes.

From score to action

Risk scores are only useful when wired to a response. Scores feed directly into the policy engine, so thresholds can escalate a graduated response — coaching at moderate risk, justification or approval higher up, redaction or a block at the top — with every step recorded in the audit trail.

FAQ

Explainable AI Risk Scoring: questions we get asked

Straight answers to what security, privacy, and compliance teams ask us first.

Is the risk score machine-learning based?

It is hybrid. The core is a deterministic weighted-factor model that is fully inspectable and tunable, augmented by behavioural anomaly detection for deviations from a user baseline. The deterministic core is what makes every score explainable and reproducible.

Can we tune the weights to our own risk appetite?

Yes. Factor weights and thresholds are configurable, and changes can be simulated in monitor mode against historical traffic before being applied.

Does it detect prompt injection?

Yes. Prompt injection, jailbreak patterns, and multi-step data-exfiltration attempts are detected as AI-specific threat classes that traditional CASB, DLP, and SWG tooling has no visibility into.

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Ready to see every AI interaction?

Talk to our team for a guided demo and a scoped, monitor-only discovery pilot.