Cybersecurity software for finance we provide

Would your security team spot an attacker inside your payment flow today?

Credential theft, account takeover, and API abuse often look like normal customer activity until the money is gone. We build cybersecurity software for financial institutions where AI links security events to transactions, so threats and fraud surface as one story, in time to stop them.

Cybersecurity Software for Finance_2880x1760

Why financial attacks slip between the security team and the fraud team

In most financial institutions, the security team watches logins, endpoints, and networks. The fraud team watches transactions. An attacker who steals credentials, changes a payee, and moves funds triggers a weak signal in each tool. Neither team sees the full attack.

Svitla builds cybersecurity software for finance that connects identity, device, API, and transaction data in one view. AI scores risk across the whole session, not just one event. Analysts get a single case with the evidence they need, and controls can step in before funds leave.

Who we serve

An in-house SOC under pressure

Your analysts handle thousands of alerts a day and miss the ones that matter. You need fewer, better alerts and faster investigations.

A global security function with tool sprawl

You run many security products across regions and business lines. You need one data layer and consistent controls for regulators everywhere you operate.

Managed security providers serving finance

You protect many financial clients from one platform. You need multi-tenant detection, reporting, and evidence for each client’s regulator.

Challenges we address

Financial firms remain among the most expensive places to suffer a breach. IBM’s 2025 Cost of a Data Breach Report puts the average financial sector breach at $5.56 million. Many of those breaches start with simple paths that tools see only in fragments.

Account takeover that looks like a real customer

Challenge: Stolen credentials pass every login check. The session looks normal until a payee changes.

How Svitla solves it: Behavioral models compare typing, device, and navigation to the customer’s usual pattern and flag mismatches.

APIs open to abuse

Challenge: Open banking and partner APIs carry high-value data. Abuse hides inside valid tokens and normal-looking calls.

How Svitla solves it: API monitoring learns normal call patterns per client and blocks scraping, enumeration, and token misuse.

Insiders with too much access

Challenge: Staff collects access over years. Unusual data pulls blend into daily work.

How Svitla solves it: Access analytics flag excess privileges and unusual data access, and feed access reviews.

Third parties nobody monitors

Challenge: Vendors connect to core systems, but their activity sits outside your monitoring.

How Svitla solves it: We log, score, and tie vendor sessions and integrations to each third party’s risk profile.

Alert floods that hide real attacks

Challenge: Each tool sends its own alerts. Analysts spend the day closing false positives.

How Svitla solves it: AI groups related alerts into one case, ranks it by business impact, and drafts the first investigation steps.

Regulatory reporting under time pressure

Challenge: Rules such as DORA set tight timelines for reporting major incidents. Evidence sits in many systems.

How Svitla solves it: Incident workflows collect timelines, impact, and evidence as the case runs, so reports are ready on time.

Our process at Svitla

STEP 1

We map your attack surface

We review digital channels, APIs, core systems, vendors, and existing security tools, and identify where data and money can leave.

STEP 2

We prioritize by business impact

We rank threats by potential loss, regulatory exposure, and customer harm, and agree on what to build first.

STEP 3

We build and test against real attack scenarios

Detections are tested against simulated attacks drawn from your threat model and frameworks such as MITRE ATT&CK.

STEP 4

We tune in production and keep improving

After launch, we track detection rates and false positives with your analysts, and update models as threats change.

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Unified security and transaction data layer

Collects logs from identity, endpoint, network, cloud, and core banking systems.

Links security events to customers, accounts, and transactions.

Normalizes data to a common schema for detection and search.

AI behavioral analytics

Profiles normal behavior for customers, staff, devices, and services.

Flags unusual logins, data access, and payment changes.

Explains which signals drove each risk score.

API security monitoring

Inventories every API, including undocumented ones.

Detects token misuse, scraping, and unusual call volumes.

Applies rate limits and blocks in real time.

Measurement and verification

Finds excess, dormant, and toxic access combinations.

Supports access reviews with risk-ranked lists.

Triggers step-up authentication on risky sessions.

Alert correlation and case management

Groups related alerts from many tools into one case.

Ranks cases by financial and regulatory impact.

Suggests next investigation steps with AI.

Automated response playbooks

Freezes sessions, holds payments, or disables accounts on approval.

Runs containment steps through your existing security tools.

Logs every action for audit.

Regulatory incident reporting

Classifies incidents against frameworks such as DORA and NYDFS Part 500.

Builds timelines, impact estimates, and notification drafts.

Tracks deadlines for each reporting step.

Is custom cybersecurity software for finance the best option for your needs?

Answer a few simple questions to find out whether you should choose a custom solution or a pre-built cybersecurity platform for finance.

1 / 10

Do your security and fraud teams use separate tools and data?

Do analysts spend most of their day closing false positives?

Do you expose APIs to partners, fintechs, or open banking apps?

Have you bought security tools that didn't fit your core banking or payment systems?

Do you need incident reports for regulators such as DORA or NYDFS?

Would AI behavioral analytics help you catch account takeover earlier?

Do third-party vendors connect directly to your core systems?

Do you run systems in several clouds or on-premises data centers?

Do you expect your digital channels or customer base to grow fast?

Important integrations for cybersecurity software for finance

SIEM and log platforms

Two-way links with tools such as Splunk, Microsoft Sentinel, or Elastic.

Identity providers

Core banking and payment systems

Transaction, payee, and account change events.

Fraud and AML platforms

Shared risk scores and cases across fraud and security teams.

Endpoint and cloud security tools

Alerts and response actions from EDR and cloud security platforms.

Ticketing and GRC tools

Cases, evidence, and control status for ServiceNow or similar tools.

More productive analysts

Fewer false positives.

Faster investigations with evidence in one place.

Lower breach and fraud losses

Earlier detection of account takeover.

Payments held while risk is reviewed.

Lower regulatory risk

On-time incident notifications.

Clear audit trails for examiners.

Fewer overlapping tools

Lower license and storage costs.

Simpler operations for your team.

Protected customer trust

Step-up checks only when risk is real.

Fewer blocked legitimate payments.

Threat modeling and regulatory scoping

We map assets, data flows, and likely attackers for each channel. We also list the applicable rules, such as DORA, NYDFS Part 500, PCI DSS, or GLBA. This shapes both detections and evidence requirements.
Data architecture and integration

We design a secure pipeline that collects logs, identity data, and transactions with low delay. We mask or tokenize sensitive data and log access.
Detection and AI model development

We build rules and behavioral models for priority attack paths. We train models on your historical data and test them against simulated attacks. We document each one for model risk review.
Response, case, and reporting workflows

We build case management, response playbooks, and regulatory reporting. Analysts test them on real scenarios before launch.
Hardening, launch, and tuning

The platform goes through penetration testing and secure code review before go-live. After launch, we tune thresholds, retrain models, and add detections as threats change.

How much does cybersecurity software for finance cost?

A focused solution for a credit union, community bank, or fintech typically costs $120,000 to $250,000. It covers one priority area, such as account takeover or API abuse, with integrations and case management. An enterprise platform with a unified data layer, AI behavioral analytics, response automation, and regulatory reporting typically costs $300,000 to $700,000. The final cost depends on channels, data volume, and integrations.

Want to understand the cost of your cybersecurity solution for finance?

Calculate the cost

Check our client success cases

Financial Performance Management Application

Project Type Big Data & Analytics

Industry Financial Services

Discover how we rewrote a financial analytics app from scratch, boosting scalability, performance, and storage capacity. Our solution enables effortless feature expansion, remote database management, and flexible infrastructure choices for the client.

Collaborative Banking Fintech Solution

Project Type Cloud Solutions, DevOps, Managed Operations Support, Mobile Development, Quality Assurance

Industry Financial Services

Discover how we significantly advanced a collaborative banking fintech solution, fostering integration between fintech and banks. Enhancements included improved web and mobile app performance, UX/UI redesign, and utilizing a microservices architecture and cloud services for scalability and optimal platform operation.

CRMa

Project Type Web Development

Industry Financial Services

Find out how we built a comprehensive platform for CRMa, incorporating a Ruby on Rails web application and a self-service tool for calculating Allowance for Loan and Lease Losses (ALLL), facilitated by a dedicated development team and a mature test automation framework, enabling extensibility and sophisticated financial analysis.

Consulting

We assess your threats, tools, and regulatory duties, then define a security software roadmap.

Engineering

We build detection, response, and reporting software around your channels and systems.

Modernization

We audit your legacy security tools and rule sets and plan the migration. The new platform adds behavioral analytics the old tools lacked. We cut over in phases and validate detection coverage after migration.

AI model retraining and support

We monitor model performance, retrain on new attack patterns, and document every change for model risk review.

Team extension

We add security engineers and data engineers to your team for new detections, integrations, or audits.

FAQ

Why build custom security software instead of buying more tools?

Most tools cover one layer well. The gap is between them, where security and fraud signals never meet. Custom software connects your existing tools around your systems and risks, so you can often use fewer products, not more.

Will this replace our SIEM?

Not necessarily. Many clients keep their SIEM for log storage and compliance. We build the finance-specific detection, correlation, and response layer on top, with two-way links.

How do you keep AI from blocking real customers?

Models score risk, and your policies decide the action. Low-risk sessions pass without friction. Higher-risk sessions get step-up checks before any block, and every decision is explainable.

How do AI models pass model risk review?

We document data, features, training, testing, and limits for every model. Performance is monitored after launch. This supports reviews under guidance such as SR 11-7.

Can the software help with DORA and NYDFS reporting?

Yes. Incidents are classified against each framework’s criteria as the case runs. The system builds timelines and drafts reports, and tracks every deadline.

How do you protect the security platform itself?

We apply least-privilege access, encryption, and full logging to the platform. It goes through penetration testing and secure code review before launch and after major changes.

What happens when a new attack type appears?

Analysts can add a rule within hours. Behavioral models also flag unusual activity that no rule describes. Confirmed new attacks feed back into model training.

Connect your security and fraud signals within one quarter

Tell us about your systems and security priorities. We’ll show you what custom cybersecurity software could change first.

Cybersecurity software for finance we provide

Would your security team spot an attacker inside your payment flow today?

Credential theft, account takeover, and API abuse often look like normal customer activity until the money is gone. We build cybersecurity software for financial institutions where AI links security events to transactions, so threats and fraud surface as one story, in time to stop them.

Why financial attacks slip between the security team and the fraud team

Who we serve

An in-house SOC under pressure

A global security function with tool sprawl

Managed security providers serving finance

Challenges we address

Account takeover that looks like a real customer

APIs open to abuse

Insiders with too much access

Third parties nobody monitors

Alert floods that hide real attacks

Regulatory reporting under time pressure

Our process at Svitla

We map your attack surface

We prioritize by business impact

We build and test against real attack scenarios

We tune in production and keep improving

Unified security and transaction data layer

AI behavioral analytics

API security monitoring

Measurement and verification

Alert correlation and case management

Automated response playbooks

Regulatory incident reporting

Is custom cybersecurity software for finance the best option for your needs?

Almost done

Thank you!

Important integrations for cybersecurity software for finance

SIEM and log platforms

Identity providers

Core banking and payment systems

Fraud and AML platforms

Endpoint and cloud security tools

Ticketing and GRC tools

More productive analysts

Lower breach and fraud losses

Lower regulatory risk

Fewer overlapping tools

Protected customer trust

Threat modeling and regulatory scoping

Data architecture and integration

Detection and AI model development

Response, case, and reporting workflows

Hardening, launch, and tuning

What would your team see if every alert told the whole story?

How much does cybersecurity software for finance cost?

Estimate the cost of your cybersecurity solution for finance

What is the main goal of the solution?

What type of organization are you?

How large is your security team?

Which area matters most?

Which systems should it connect to?

Which AI capabilities do you need?

What is your timeline?

What is your budget range?

Almost done

Thank you!

Check our client success cases

Financial Performance Management Application

Collaborative Banking Fintech Solution

CRMa

Consulting

Engineering

Modernization

AI model retraining and support

Team extension

FAQ

Why build custom security software instead of buying more tools?

Will this replace our SIEM?

How do you keep AI from blocking real customers?

How do AI models pass model risk review?

Can the software help with DORA and NYDFS reporting?

How do you protect the security platform itself?

What happens when a new attack type appears?

Connect your security and fraud signals within one quarter