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What AI and traditional detection do differently
Traditional threat detection commonly relies on defined rules, signatures, thresholds, and control logic. These methods can tie an alert to a known pattern or condition, but may miss activity that does not match established rules and require updates as threats and systems change.
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AI-enabled detection can learn patterns from data and flag anomalies across varied signals. Depending on the system, it may also help analysts prioritize or summarize alerts. Those capabilities are potential benefits, not guaranteed results; the model’s output still needs validation and human review.
| Decision area | AI-enabled detection | Traditional detection and controls | What a bank should verify |
|---|---|---|---|
| Detection method | Learns patterns from data and may surface anomalies | Uses defined rules, signatures, thresholds, and established controls | Which threat types and data sources are covered, and which are not? |
| Analyst workflow | May prioritize or summarize signals | Often produces alerts tied to explicit rules or control logic | Can an analyst reproduce why an alert fired and take appropriate action? |
| Change over time | Data, model, or concept drift can degrade behavior; models need lifecycle monitoring | Rules and signatures also need updates as threats and systems change | Who owns updates, validation, monitoring, and rollback? |
| Security risks | May face evasion, poisoning, privacy, model-security, and opacity concerns | May miss threats outside known patterns and depend on manual updates | How are misses, false positives, and adversarial tests measured? |
| Dependencies | May depend on cloud services, vendor models, sensitive telemetry, and specialized skills | May depend on vendor feeds, rules, and integration with existing infrastructure | What data leaves the institution, and what third-party controls apply? |
| Governance | Needs controls for models, data, access, and human oversight | Needs control ownership, change management, and evidence of effectiveness | Are responsibilities and records appropriate for the bank’s risk profile? |
This is an evaluation guide, not a product ranking: the available sources do not provide comparable benchmark scores for AI and traditional detection at commercial banks.
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What the evidence does—and does not—show
A May 2024 Bank for International Settlements paper reports that cybersecurity experts at major central banks expected AI tools to improve threat detection and reduce response time. It also identifies risks including unauthorized data disclosure and social engineering, as well as demand for cybersecurity and AI skills. These are expert expectations, not measured product results at commercial banks; the paper does not establish a numerical detection-rate advantage.
The Federal Reserve’s July 2025 Cybersecurity and Financial System Resilience Report describes AI as having potential to strengthen intrusion detection and data-loss protection. It also warns that attackers can use AI to scale phishing, voice cloning, or malware activity. Potential defensive value therefore does not remove the need to prepare for AI-assisted attacks.
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A separate example should not be mistaken for a bank cybersecurity comparison: in a November 22, 2024 speech, Federal Reserve Governor Michelle W. Bowman reported that Treasury fraud-prevention and recovery attributed to Treasury fraud-detection tools, including machine-learning AI, exceeded $4 billion in fiscal year 2024, including $1 billion related to identifying Treasury check fraud. That is a Treasury program figure about fraud prevention and recovery, not a head-to-head test of bank threat-detection systems. Bowman cautioned that “AI will not completely ‘solve’ the problem of fraud—particularly as fraudsters develop more sophisticated ways to exploit this technology.”
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Detection is only one part of cybersecurity. Interagency guidance from the Federal Reserve, FDIC, and OCC treats authentication and access as risk-management matters for employees, customers, third parties, service accounts, applications, and devices. A bank’s risk assessment should inform its authentication choices, while threat identification can draw on information-sharing organizations and actual or attempted incidents. How guidance applies can vary with an institution’s operational and technological complexity and its risk appetite.
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AI alerts cannot substitute for controls that limit access, identify who or what is authenticating, and contain an incident. Nor does an anomaly automatically establish malicious activity: analysts need enough context to investigate it and decide what response is proportionate. Retaining established controls provides defenses that do not depend on a model being correct in every case.
AI-specific risks to include in the threat model
AI systems introduce lifecycle and security concerns that may be difficult to detect through ordinary software testing alone. NIST’s AI Risk Management Framework Appendix B notes that traditional risk frameworks do not comprehensively address some AI risks, including evasion and other machine-learning attacks. NIST AI 100-2e2025, published March 24, 2025, provides terminology for adversarial machine-learning concepts, lifecycle stages, attacker goals, and mitigation challenges; it is a threat-modeling resource, not a bank compliance mandate.
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- Drift: Changes in data, behavior, or operating conditions can make a model’s outputs less reliable over time. Define monitoring thresholds and who can pause or roll back a model.
- Adversarial manipulation: Threat actors may attempt to evade detection or manipulate the data and processes a model relies on. Include these possibilities in testing, rather than treating ordinary accuracy tests as sufficient.
- Opacity and uncertainty: If analysts cannot understand or investigate why an alert was produced, they may struggle to validate it or explain the resulting action. Establish what evidence and explanation the workflow must retain.
- Privacy and disclosure: Telemetry may contain sensitive information, and a service may involve external providers. Determine what data is collected, where it is processed, who can access it, and whether it is disclosed outside the institution.
- Operational dependency: Cloud services, vendor models, integrations, and specialized skills can add failure points. Plan how detection and incident response continue if a component is unavailable or its output is no longer trusted.
- Attacker use of AI: Phishing, voice cloning, and malware activity may be scaled or made more convincing with AI, as the Federal Reserve’s July 2025 report notes. Include these threats in awareness, detection, and response planning.
How a bank can evaluate the combined system
There is no source-backed universal winner or regulator-prescribed scorecard. The following evaluation approach translates the cited guidance and AI-risk concerns into institution-specific checks.
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- Set the scope from the risk assessment. Identify the assets, users, services, and threat scenarios the system is meant to protect. Include the relevant data sources and existing controls; do not judge a tool only by the number of alerts it generates.
- Establish a baseline. Record how the current rules, signatures, access controls, and analyst processes perform against the scenarios in scope. Document known coverage gaps and how updates are managed.
- Test on relevant local data. Compare AI-assisted and existing workflows using representative data and scenarios. Measure detection quality, false positives, missed threats, and response time under the bank’s conditions. Do not generalize results beyond the tested environment.
- Check alert explainability and actionability. Have analysts investigate outputs without assuming the model is right. Verify they can identify supporting evidence, reproduce why an alert was raised, and escalate or dismiss it with a recorded rationale.
- Exercise resilience and adversarial cases. Test failure modes, drift monitoring, rollback, and relevant evasion or data-manipulation scenarios. Define who can suspend the system and how the bank maintains detection and response during an outage or suspected compromise.
- Review data handling and third parties. Map telemetry flows, vendor and cloud dependencies, access rights, and applicable third-party controls. Confirm what information leaves the institution and what happens when a provider, model, or integration changes.
- Assign ongoing ownership. Name the teams responsible for model and data changes, rule updates, validation, access, incident handling, and evidence retention. Review performance after deployment rather than treating initial acceptance as permanent approval.
Useful comparison measures include detection quality, false positives and misses, time to response, analyst workload, explainability, privacy, drift, resilience, integration effort, staffing needs, and third-party exposure. The bank should define how each is measured for its own threat scenarios and risk profile; the cited guidance does not prescribe universal target values.
Which U.S. guidance and frameworks to consult
U.S. federal financial regulators’ oversight of AI relies primarily on existing laws, regulations, guidance, and risk-based examinations, according to a Government Accountability Office report published May 19, 2025. That report covers financial services broadly, including credit unions; it is not a new bank-specific cybersecurity rule.
For cybersecurity self-assessment, banks should not rely on the former FFIEC Cybersecurity Assessment Tool as a current tool. Federal Reserve SR 24-7, dated November 22, 2024, said the CAT would no longer be updated and would be removed from the FFIEC website on August 31, 2025. That date has passed. The letter identifies the following resources:
- NIST Cybersecurity Framework 2.0
- CISA Cybersecurity Performance Goals
- Cyber Risk Institute Cyber Profile
- CIS Critical Security Controls
The Federal Reserve states, “The Federal Reserve does not endorse any particular cybersecurity self-assessment tool,” and advises using tools commensurate with risk. These resources can inform a program assessment; they do not establish that one detection technology is superior.
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