Decide-time contribution
Which rules, lists and score components fired—stored when the decision is made so the recorded contribution can be inspected later.
Platform · 04 Explain
Every decision has a story. Keep the evidence with it.
Help analysts understand the finding, risk leaders explain the response and reviewers inspect the evidence. SENTR connects AI explanations with recorded score attribution, human overrides and exportable decision history.
- Contribution stored with the decision
- Overrides keep a written reason
- AI explanations with inspectable attribution
Machine learning & AI
Learn your patterns. Make the findings understandable.
Authored rules cover patterns your team knows to look for. Unusual behaviour can still deserve attention when no existing rule describes it.
SENTR’s customer-specific anomaly model learns normal activity from clean signals, independently of authored rule outputs. It returns a separate risk score and contributing signals. Training runs overnight once sufficient suitable data is available; evaluation and training are different processes.
SENTR Product viewDemo data
Enlarge view The matched rule, its impact and the recorded reason—together on the event.
The explanation has something concrete behind it: the matched check and its contribution.
Go deeper: Machine learning & AI
- Inspect both scoring paths
- Rule contributions and AI score breakdowns remain distinct. Analysts can inspect severity, exact score impact, confidence and the reasons that contributed most.
- Read the explanation, then verify it
- Generative AI translates relevant recorded findings into natural language. Attribution is stored for every evaluated event; quiet events need not generate a prose narrative. The language model explains the score, not the block decision.
- Keep feedback accountable
- Validated outcomes and corroborating signals inform the customer-model learning process. A reviewer’s override is not blindly accepted as training truth. Sector and global model tiers are available where agreed for your deployment.
Give analysts readable reasons without hiding the evidence behind them. AI scoring, the AI rule builder, the ML pipeline and automatic feature engineering extend this foundation. Agree their configuration and access for your deployment.
Governance & operability
Audit the controls—not just the decisions they produce.
When someone challenges an outcome, the question may be who changed the policy, not simply what score the event received.
SENTR records configuration changes with user attribution and timestamps, including rules, policies, lists and reason codes. Decision explanations and reasoned overrides keep system findings and human judgement distinguishable.
SENTR Product viewDemo data
Enlarge view A human decision change requires a reason. The dialog identifies what will be recorded in the event history.
Keep system findings and human judgement distinguishable.
Go deeper: Governance & operability
- Query the operational trail
- Audit logs are a report data source alongside events, rules, cases, profiles and integrations. Review changes and export evidence without treating the audit trail as a separate support request.
- Operate access and delivery
- Organisation and team settings, access policies, scoped API credentials and webhook delivery traces support the people running the deployment. Confirm authentication and data-handling requirements during technical and security review.
- Keep enforcement explicit
- SENTR returns the decision. Your connected application enforces the business response; configured webhooks handle downstream effects. Qualified Shadow Mode keeps SENTR out of production enforcement.
Give operators, engineering and governance a shared evidence trail. Security commitments are deployment-specific; SENTR does not claim certifications it does not hold.
Answer the question without reconstructing the week.
Scattered logs force people to reconstruct why a payment was blocked three systems later. Governance stakeholders need attribution that travels with the decision—and a clean line between what the system computed and what a human changed.
Read the diagram
A sample payment event has score 72 and a review response under auth.default. Selected contributions are device velocity +18, first-seen IP +12 and payment-method reuse +8. They show part of the attribution, not a sum to 72. Recorded findings are the evidence; an AI explanation makes them readable.
From AI explanation to the underlying record.
The calculation, the explanation and the human decision
AI explanation, grounded in attribution
Generative AI translates relevant score findings into plain language. The recorded attribution remains available to inspect; the generated prose is not the decision itself.
Human authorship
Overrides keep a written reason on the same record. Machine and human contributions stay distinct on purpose.
Your responsibility as deployer
SENTR supplies operating evidence. Your organisation remains accountable for how automated decisions are used under applicable law.
See the evidence trail in practice.
Illustrative workflow
Blocked withdrawal, reasoned override, one record
A withdrawal is blocked on rule contribution plus policy threshold. The on-call reviewer overrides to allow after confirming a known travel pattern, with a written reason.
- Withdrawal
- Block (rule + policy)
- Travel pattern check
- Override to allow
Nobody invents the story from a ticket thread six weeks later.
Hosting, subprocessors and certification status (none held) are on Security & data.
The reason is there at decision time.
You do not need to open an investigation to understand why an event was evaluated the way it was.
Recorded attribution is kept for evaluated events. Natural-language rendering describes relevant rule or model findings; quiet traffic need not generate a prose narrative. Keep the original system decision distinct from a person's later override.
The product views above show a matched-rule contribution and an override dialog, not a completed AI narrative or a saved outcome from one continuous case. Historical reconstruction depends on the inputs, configuration and model context retained for your deployment.
Inspect the two scoring paths → · Explore reports and audit evidence
Keep the work connected
What happens next?
- What comes in
- Recorded findings, their contributions and the decision taken.
- What moves forward
- Evidence your team can inspect, with human changes distinguishable from system findings.
When a finding needs investigation, follow the connected evidence.
Continue to InvestigateSENTR.Tower retains decision visibility and explanations. SENTR.Citadel adds deeper configuration and custom reporting—not a monopoly on understanding the decision.
Compare the exact controls ↗Make the next difficult question easier to answer.
Read Security & data — or Book a Session when governance is on the committee agenda.