Rule-based tools only catch attacks they have already seen. The breach that costs you is the one with no signature — the insider moving slowly, the novel fraud pattern, the lateral movement that reads as normal traffic. Banao builds machine-learning anomaly detection that learns your environment's baseline, scores deviations in real time, and automates triage so your security team stops drowning in false positives. It is the same detection and monitoring stack Banao has run on its own 300-person operation since 2017.
Most teams already have a SIEM, a firewall, and a rules engine — and still get surprised. The gap is detection of the unknown: behavior no rule was written for. Banao closes that gap with models that profile users, transactions, and network flows, then flag the deviations that matter and suppress the noise that doesn't. We have shipped fraud and anomaly systems at payments scale for PhonePe and enterprise security for CP Plus, and we run the same telemetry and detection internally across 300 engineers in India, UAE, UK, and US.
Detection, scoring, and response — engineered around your data, your risk surface, and your compliance obligations, then integrated with the SIEM and SOC tooling you already run.
Book a 45-min scoping call
Catch intrusions before they spread
Models that watch network, endpoint, and cloud telemetry for the patterns signature tools miss — tuned to your baseline so a new attack stands out instead of hiding in normal traffic.
Spot the insider and the slow breach
Behavioral baselines for every user and system that surface account takeover, privilege abuse, and lateral movement weeks earlier than rule-based alerts.
Stop fraud without blocking real customers
Real-time transaction scoring for payments, lending, and e-commerce — the same class of system Banao has shipped at PhonePe scale, built to catch new fraud patterns while holding false declines down.
Cut time-to-contain from hours to minutes
Risk-scored playbooks that alert, isolate, and remediate automatically, so analysts spend time on real incidents instead of triaging noise.
Close the gaps attackers actually use
Detection for ransomware, malware, and unauthorized access across devices and cloud workloads, mapped to how your environment is actually configured.
Stay audit-ready without the fire drill
Continuous control monitoring and evidence collection for GDPR, HIPAA, PCI-DSS, and RBI frameworks — reporting generated from live telemetry, not assembled the week before an audit.
Find the weakness before an attacker does
AI-assisted vulnerability discovery and attack simulation that prioritizes the exposures most likely to be exploited in your stack, not a generic CVE dump.
One detection layer across your tools
Detection and scoring modules wired into your existing SIEM, SOC, and enterprise systems so coverage is unified instead of siloed across point products.
Industries We Protect with Cybersecurity AI
Retail & E-commerce
Score every transaction for payment fraud and account abuse in real time, holding false declines down so legitimate buyers check out clean.
EdTech & Learning
Detect account takeover and credential abuse across learning platforms before student and institutional data is exposed.
Healthcare & Life Sciences
Flag unauthorized access to patient records and surface HIPAA-relevant anomalies in clinical systems as they happen.
Banking & Finance
Catch fraud and AML anomalies that static rules miss, with transaction monitoring tuned to your disbursal and payment flows.
Manufacturing & Logistics
Monitor OT and supply-chain systems for anomalous behavior that signals compromise or tampering before operations are disrupted.
Telecom & Utilities
Detect service fraud and infrastructure attacks across high-volume networks where rule-based alerting can't keep pace.
Recent Work
Rodi AI Insurance
Rodi needed to insure high-value items but relied on manual valuation and claim review that was slow and inconsistent. Banao built an AI insurance platform that automates item valuation and applies real-time risk scoring to surface suspect claims as they arrive. Assessors now work from a risk-ranked queue instead of a flat inbox, with protection decisions made on live data rather than periodic manual checks.
Ai Contract Review
Legal teams were reading every contract line by line, making review slow and inconsistent across reviewers. Banao built an AI contract-review system that extracts clauses and flags anomalous or high-risk terms against a defined playbook. Review time dropped sharply and risky clauses are now caught consistently instead of depending on which reviewer opened the document.
Justicia Lab AI
Immigrant-justice nonprofits wanted to adopt AI but had no safe, structured way to evaluate where it fit. Banao designed and built a non-profit AI lab that lets legal-aid and advocacy organizations test AI use cases under clear governance. Advocacy groups can now adopt AI responsibly with guardrails in place, instead of avoiding it or using it without oversight.
Data Tagging System
A data-heavy enterprise was categorizing and tagging millions of records by hand, creating backlogs and inconsistent metadata. Banao built an AI and NLP tagging pipeline that classifies content automatically and routes low-confidence records for human review. Documents process faster, search relevance improved, and tagging accuracy no longer depends on manual effort across millions of records.
We begin by thoroughly understanding your critical assets, potential risks, and security requirements. Map threat scenarios, identify vulnerabilities, and ensure compliance with industry standards to build a strong security foundation. Why this matters: most vendors start modeling before they know what's worth protecting, so they detect noise and miss the assets an attacker actually targets.
Collect security logs, transactional data, and user behavior analytics to train advanced anomaly detection and fraud prevention models. Leverage AI to detect subtle patterns and predict potential threats in real time. Why this matters: a fraud or anomaly model is only as good as the baseline it learns; skip clean historical data and you ship a detector that alarms on everything and catches nothing.
Simulate real-world cyber attacks to test model performance, validate detection accuracy, and minimize false positives. Ensure your systems are resilient against evolving threats and sophisticated intrusion attempts. Why this matters: a model that looks accurate offline fails the first novel attack — we red-team it before production so false positives and blind spots surface in testing, not in your SOC.
Seamlessly integrate AI-driven detection modules with existing security platforms. Automate alerting, incident management, and response workflows to enable rapid mitigation and reduce manual intervention. Why this matters: detection without automated response just moves the bottleneck to your analysts; we wire alerts into containment so time-to-contain actually drops.
Continuously monitor the threat landscape, retrain AI models with new data, and refine detection capabilities. Ensure your cybersecurity infrastructure evolves to counter emerging risks and maintain optimal protection. Why this matters: attack patterns shift monthly, and a detection model that isn't retrained quietly decays until it misses what it used to catch.
Client Voices: Security & Fraud Prevention
Rohit Sharma
Head of IT Security, SafeBank
Sarah Lee
CISO, MedTech Systems
Fraud detection at speed
Banao's anomaly models flagged fraud patterns our rules engine never caught, and they brought our false-positive rate down enough that the team could finally act on every alert. Compliance reporting now runs off live telemetry instead of a month-end scramble.
Here is what you will get for submitting your contact details.
45 minutes of free consultationA strict non-disclosure agreementFree market & competitive analysisSuggestions on revenue models & planningDetailed feature list documentNo obligation proposalAction plan to kick start your projectWe deployed an anomaly detection tool before and it drowned us in false positives. How is this different?
That's the most common failure mode, and it usually comes from a model trained on too little of your own data with no tuning loop. We baseline against your real traffic, validate against simulated attacks before go-live, and keep a feedback loop that retrains on what your analysts mark as noise — so you get fewer, higher-confidence alerts your team will actually act on.
How do you stop the model from missing novel attacks or flagging the wrong things?
We combine behavioral baselines with attack simulation and a human-in-the-loop review queue. Novel attacks surface as deviations from normal rather than known signatures, and low-confidence detections route to analysts instead of auto-blocking. We track both catch rate and false-positive rate and tune for the balance your operation can sustain.
Can this work with our existing SIEM and SOC tooling?
Yes. We're stack-agnostic and integrate detection and scoring modules into the SIEM, SOC, and cloud platforms you already run — no rip-and-replace. Week one is an integration audit so the detection layer sits on top of your current investment, not beside it.
Who owns the data and the models you build?
You do — 100%. Custom code, trained models, and training data are yours. For regulated environments we sign DPAs and can train entirely inside your VPC or perimeter, so Banao engineers never touch raw customer data.
Should we just build this with our in-house team?
If you have ML engineers, security analysts, and data engineers with spare capacity, you can — most teams take 12-18 months because the talent is hard to hire and the project competes with day jobs. We compress that to weeks because detection systems are what we build, and we hand over documented models your team can own and extend. Several clients started in-house and brought us in six months later; we'd rather save you those months.
Is this compliant with our regulatory obligations?
We design to GDPR, HIPAA, PCI-DSS, and RBI cybersecurity frameworks as a day-one constraint, not an end-of-project checklist. Audit evidence and reporting are generated from live telemetry, and the first deliverable in regulated engagements is a security and compliance architecture review your CISO co-signs.
What does this cost and how long does it take?
A scoped pilot tied to one metric — fraud catch rate or false-positive reduction — typically runs 8-12 weeks. Most engagements land in the $80K-$250K range depending on data volume, integrations, and compliance scope; smaller pilots start lower. Book a 45-min scoping call and we'll map your scope and give you a firm number.
