AI Security Risk Prediction Agent
Spot anomalies in encrypted financial and security workloads before they become breaches. Behavioral risk scoring, for fintech and cybersecurity, without ever decrypting payloads.
AKRUM's AI Security Risk Prediction Agent is one of five prebuilt guardrails. Turn it on to score risk across encrypted financial and security workloads on every agent action, with no rules to write. AI may assist with interpretation and orchestration, but the deterministic policy engine owns every ALLOW, DENY, or CHALLENGE decision.
Overview
What this AI Agent does
Scores each event on your AKRUM-protected fintech and security workloads, data access, key usage, decryption, and exfiltration signals, from 0 (benign) to 100 (critical), then returns an ALLOW, DENY, or CHALLENGE decision. The deterministic engine owns the verdict, not an AI model.
Every point in the score comes from a named, weighted signal, operation type, source-jurisdiction risk (public FATF/OFAC listings), volume, velocity, production scope, and compound exfiltration patterns (mapped to MITRE ATT&CK), so any decision is explainable line by line to an auditor, with no black-box ML.
Enforced inline through the Guardrails SDK: allow proceeds, deny is blocked, challenge triggers step-up authentication, and every decision is signed with post-quantum ML-DSA and written to a tamper-evident log. Analysis happens without ever decrypting the payload.
How it works
A four-step guardrail flow
Send the event
Your agent or app sends the event and its context (operation, jurisdiction, volume, service) to the guardrail. Payloads stay encrypted.
Score the risk
The deterministic engine weighs named signals and assigns a 0 to 100 risk score. The AI never makes the decision.
Return a decision
It returns ALLOW, DENY, or CHALLENGE, with the signals and score that drove it.
Enforce inline
Allow proceeds, deny is blocked, challenge triggers step-up, and the signed decision is logged, tamper-evident.
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.
Where it’s used
Real-time risk scoring, across fintech and cybersecurity.
Fintech
Money movement and identity are the two things attackers want most. Score risk on your encrypted financial workloads, flag unusual access and high-risk activity while data stays encrypted.
Cybersecurity
When perimeter and detection controls are bypassed, the data itself has to stay protected. Flag insider misuse, cloud-based attacks, and exfiltration patterns before they escalate.
The agent scores security and access events on encrypted workloads. It is not a standalone fraud model.
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.