Banks face both internal technical attacks against digital channels and external abuse of their brand. Internet-banking logs can contain signals of SQL injection, credential stuffing, and API data exfiltration, while the public internet can contain phishing pages, fake support accounts, cloned apps, malicious APKs, lookalike domains, and leaked customer data. Many teams need faster detection, classification, investigation, and response workflows.

Security teams have access to more logs and external threat data than before, but manual analysis is slow. AI-assisted detection rules, anomaly detection, entity correlation, and automated enrichment can improve SOC triage and digital risk response while keeping analysts in control.

A security analytics and digital risk protection product for banks. It can start as a log-analysis engine that detects attack patterns and reconstructs an attack chain, or as a DRP platform that monitors domains, social networks, messengers, app stores, marketplaces, and dark-web sources for brand abuse. The strongest products will connect detection, evidence, prioritization, and response/takedown workflow.

• Log analytics: detect credential stuffing, SQL injection, and data exfiltration from web-server and backend logs.
• Attack-chain reconstruction: show start time, end time, duration, attacker IP, request volume, and data volume by attack type.
• SOC rule builder: generate reusable detection rules and analyst explanations.
• Digital risk monitoring: find phishing sites, fake bank pages, unofficial Telegram bots/channels, fake support accounts, and cloned mobile apps.
• Domain threat monitoring: typosquatting, homoglyph domains, and product-name domain combinations.
• Takedown workflow: track incidents from discovery through validation, registrar/hosting/app-store request, and closure.

• Cybersecurity, SOC, threat-intelligence, or anti-fraud team
• Experience with logs, detection engineering, web security, OSINT, or brand protection
• Ability to build explainable security analytics rather than black-box alerts
• Strong understanding of banking security, compliance, and incident workflows

• Upload or ingest web-server/backend logs in a defined format
• Detect at least three attack types: credential stuffing, SQL injection, and data exfiltration
• Attack summary with attacker IP, time window, duration, request count, and response-size volume
• Detection rules and analyst notes
• External-threat monitoring prototype for domains/social/app-store records using sample data
• Incident severity classification: critical, high, medium, low
• Case management view for validation and takedown tracking

• The prototype finds the suspicious IPs and correctly separates different attack patterns.
• A SOC analyst can understand why an alert was generated and what evidence supports it.
• The system produces reusable rules or logic that can be applied to future logs.
• External brand-abuse incidents are prioritized by customer and financial risk.
• The response workflow tracks status until the incident is removed or closed.

• The system must avoid encouraging offensive misuse; focus on defensive detection and response.
• Do not expose sensitive logs or customer data in dashboards unnecessarily.
• Critical incidents should support manual analyst validation before response actions.