AI Security Risk Prediction Agent
Spot anomalies in encrypted financial transactions before they become breaches. Behavioral risk scoring without ever decrypting payloads.
Overview
What it does
Consumes encryption metadata, access logs, and key-usage telemetry across your AKRUM-protected fintech workloads and assigns each event a risk score from 0 (benign) to 100 (critical).
Grounded in patterns documented in publicly disclosed financial breach reports, OWASP cryptographic failures, and NIST incident data. Flags impossible-travel key usage, anomalous decryption volume, and configuration drift across environments.
Returns a structured risk verdict suitable for piping into your SIEM, fraud engine, or human-in-the-loop review queue.
How it works
A 4-step scoring flow
Stream telemetry
POST encryption telemetry events (key id, op type, geo, timestamp, byte volume).
Build baselines
Agent maintains rolling behavioral baselines per key and per service account.
Score event
Each event is scored against the baseline plus known fintech attack signatures.
Return verdict
Returns risk score + reasoning + suggested action: allow / require step-up auth / block.
Sandbox
Try it live
Adjust event parameters and run the same deterministic risk scorer the API runs.
{ "risk_score": 5, "verdict": "allow", "reasoning": [ "No anomalous indicators detected." ], "matched_signatures": [], "scored_at": "2026-01-15T10:00:00.000Z", "model_version": "risk-v1.4" }
Simulated response — no real inference is performed.
Data sources
Grounded in public security incident data
- Published rule set encoded in the engine: operation type, jurisdiction, volume and velocity weights (every score returns the weights that produced it)
- OWASP Top 10 — A02:2021 Cryptographic Failures (rule design reference)
- FFIEC IT Examination Handbook — Information Security (rule design reference)
- Planned — not yet connected: live threat-intel feeds (NVD, CISA KEV, MITRE ATT&CK) and per-tenant behavioural baselines
All sources are public and updated as new incident data is published.
How does AKRUM score transaction risk without decrypting the data?
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The agent scores encryption telemetry — key id, operation type, geography, timestamp, byte volume — rather than the transaction body. Behavioral baselines per key and service account let it flag anomalies without ever needing the cleartext payload.
What kinds of anomalies and fraud signals can it detect?
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Unusual key usage, impossible-travel access, sudden volume spikes, off-hours decryption, geo drift, and behavioral deviations from each key's rolling baseline. Together these surface exfiltration attempts and compromised credentials in near real time.
How does it fit into an existing fintech or payments stack?
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Stream telemetry events to /v1/agents/risk-prediction/score from your payments service, KMS, or SIEM. Responses are structured JSON, easy to drop into rules engines, fraud platforms, or step-up authentication flows alongside your current vendors.
What latency should I expect on real-time scoring?
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Scoring runs in milliseconds and is designed to sit inline with payment authorization or session decisions without adding meaningful user-visible latency. Higher-throughput tiers add dedicated capacity and SLA guarantees for production traffic.
Is there a free sandbox to evaluate it?
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Yes. The sandbox on this page lets you submit synthetic telemetry and see the agent score it live. You can validate the response shape and signal quality before issuing a production API key or wiring it into payment authorization.
Ready to integrate?
Get an API key and start calling AI Security Risk Prediction Agent in minutes.