AI Cybersecurity Training for Financial Services Security Teams

Financial Services Security Teams Face AI Risk on Two Fronts

Banks and other financial institutions are among the most aggressive AI adopters in the enterprise: fraud and AML transaction monitoring, KYC document review, customer-facing chatbots, and increasingly, agentic LLM workflows in operations. Each deployment is also a new attack surface that the security team is expected to defend, validate, and explain to internal audit and regulators.

At the same time, attackers are using AI to scale phishing, voice cloning for wire-fraud social engineering, and adversarial probing of fraud detection models. Security teams that lack hands-on AI skills cannot meaningfully red-team the systems their institution is deploying, nor evaluate vendor claims about AI in security tooling.

Most generic AI training is built for data scientists or software engineers. It does not address adversarial testing, supervisory expectations under SR 11-7, or how machine learning sits inside a regulated security operations workflow.

What Financial Services Security Teams Will Learn

GTK Cyber courses teach AI and data science skills grounded in financial services security operations:

• AI red-teaming for production systems: Test LLM-powered applications and ML models for prompt injection, jailbreaks, training data poisoning, and model extraction. Map findings to MITRE ATLAS and the OWASP Top 10 for LLM Applications.
• Adversarial ML against fraud and AML models: Build evasion attacks against gradient-boosted classifiers and anomaly detectors representative of transaction monitoring systems. Quantify model robustness under realistic adversary budgets.
• Machine learning for SOC and fraud signal triage: Apply scikit-learn, pandas, and Jupyter to authentication logs, network telemetry, and transaction streams. Build anomaly detection pipelines that produce reviewable, auditable findings.
• AI governance under SR 11-7 and beyond: Validation, ongoing monitoring, and documentation for ML and AI models used in security. Operationalizing the NIST AI Risk Management Framework, DORA operational resilience expectations, and EU AI Act risk classifications inside an existing model risk management function.
• Generative AI for analyst workflows: Build retrieval-augmented LLM workflows for threat intelligence triage, alert summarization, and incident response, with explicit handling of data residency and PII constraints common in regulated environments.

Built for Regulated Environments

GTK Cyber training is designed to fit inside a financial institution’s security and compliance constraints:

• On-site delivery. Courses can be delivered at your facility. No requirement to send personnel to a public venue or share data outside your perimeter.
• Offline lab environment. The Centaur VM bundles all tools and datasets locally. Labs run without external network access, which suits restricted-network and PCI-scoped training environments.
• Custom curriculum. Engagements are scoped around your institution’s tooling (Splunk, Elastic, Snowflake, native cloud SIEM), threat model, and supervisory expectations. Sessions can include regulatory framing for the jurisdictions you operate in, including DORA, NYDFS Part 500, and the EU AI Act.
• Vendor-neutral. GTK Cyber does not sell AI security products. Training is built around techniques and frameworks, not a tool ecosystem.

Credentials

GTK Cyber is a long-standing training partner at Black Hat USA, Asia, and sector events, with four courses scheduled at Black Hat USA 2026 in Las Vegas. Enterprise clients include ING and Booking.com. Charles Givre, lead instructor, is PMC Chair of Apache Drill and a co-author of published work on security analytics. Summer Rankin has 30+ peer-reviewed publications applying machine learning across regulated domains.

Students leave with working Python notebooks, red-team test plans, and detection models calibrated against their own environment. Not certificates. Working artifacts they can run the next week.