Which pitfalls—model bias, false positives/negatives, data quality, regulatory constraints—often impede AI-based security tools, and how can they be mitigated in a financial-services context?

My vote will go to data quality because all others are dependent on this. Now if you will take financial services there is very less chance of issue in data quality, because of its nature. So here you may face false positive/negative and model bias. Only option is human in the loop, which I feel everyone ignore with reason , "if I have to do then why should I review. I donot want AI I will do this."

Threshold Tuning: Adjust sensitivity based on risk appetite.
Hybrid Approach: Combine AI with rule-based checks for critical actions.
Continuous Feedback Loop: Incorporate human review outcomes to retrain models.
Data Governance: Enforce strict data validation and cleansing.
Real-Time Sync: Integrate with authoritative identity sources (e.g., HR systems).
Monitoring Pipelines: Detect anomalies in data ingestion early.
Privacy by Design: Minimize PII usage; apply encryption and anonymization.
Audit Trails: Log all AI-driven decisions for compliance review.
Model Governance: Document model lifecycle, versioning, and validation steps.
Human-in-the-Loop: For high-risk actions, require manual approval.
Scenario Testing: Simulate edge cases (e.g., insider threats, credential theft).
Continuous Monitoring: Deploy dashboards for real-time performance and drift detection.

In my experience, these biggest pitfalls can be mitigated by:
– Using diverse, representative datasets to reduce bias.
– Combining AI with human-in-the-loop validation to filter false positives/negatives.
– Investing in data governance and quality controls to ensure clean inputs.
– Building with compliance by design and maintaining clear audit trails to satisfy regulators.
Ultimately, the most effective approach is AI augmentation, not replacement — pairing automation with skilled professionals to balance scale, accuracy, and accountability.

I wrote a blog post about this topic:
https://www.ismc.at/?p=76

Data, especially biased data, is a huge concern. Companies just starting should consider "synthetic data" to test the integrity of their AI. Another pitfall not mentioned is worker bias towards AI, will they use it in the first place?