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In a November 7, 2023 VentureBeat interview, Assaf Keren, then PayPal’s chief information security officer and vice president of enterprise cybersecurity, described generative AI as dual-use technology: criminals can use it to make attacks more convincing and evasive, while defenders can use it to investigate threats, adapt fraud controls and reduce customer friction. The interview’s strongest evidence, however, concerns PayPal’s broader artificial-intelligence and machine-learning program—not a disclosed production large-language-model fraud system.
What Keren actually argued
Keren’s point was not that an LLM should independently decide which payments to approve. It was that AI can help a large security and risk operation process more signals, identify changing attack patterns and give analysts faster access to useful evidence.
That distinction matters. Generative AI creates or transforms text, code and other content. Predictive machine learning estimates risk, classifies events and detects anomalies. A practical payments architecture may use both: specialized fraud models and rules for time-sensitive decisions, with generative tools assisting the people who investigate, explain and respond to those decisions.
The attacker-and-defender trade-off
Attackers can use generative systems to create synthetic identities, vary malware and tailor social-engineering content. Defenders can use comparable technology to summarize alerts, correlate logs and case notes, draft queries, explain suspicious behavior and propose response steps. The advantage goes to the organization that can use automation without surrendering data control or human accountability.
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PayPal’s established AI foundation came first
In the interview, PayPal said it had used AI for more than a decade across fraud reduction, customer protection, risk management, personalization and commerce. Keren also described transformer-based deep learning and a development process in which deep-learning models could move from training into production in roughly two to three weeks. That is a PayPal-specific operating claim, not a universal implementation benchmark and not evidence that generative AI itself delivered the speed.
The scale cited in the 2023 interview helps explain why mature infrastructure matters. PayPal reported approximately 430 million active accounts, 35 million merchants and more than 200 petabytes of payments data at that time. Those figures are historical and should not be treated as current 2026 totals.
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What the reported performance numbers mean
| Measure | Reported detail | How to interpret it |
|---|---|---|
| Total payment volume | $712 billion in 2019, rising to $1.36 trillion in 2022 | Payment volume, not revenue; figures attributed to PayPal in the interview |
| Loss rate | Nearly 50% lower from 2019 to 2022 | Keren attributed part of the reduction to advances in AI algorithms and technology; no independent causal study was provided |
| Model deployment | About two to three weeks for deep-learning models | An operational claim about PayPal’s process, not proof that a generic GenAI system can match it |
The numbers show the potential value of a long-running data, modeling and feedback program. They do not show that a newly deployed chatbot caused the loss-rate reduction, nor that another company can reproduce it without comparable data quality, engineering, fraud expertise and controls.
Where generative AI can fit in a payments security operation
1. Analyst investigation and triage
A model can turn a high-volume alert queue into an initial timeline, summarize related events and point an analyst toward relevant evidence in logs, tickets, threat intelligence and prior cases. The output should remain linked to the underlying records so a reviewer can verify it.
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2. Search, queries and response preparation
Natural-language interfaces can translate an analyst’s question into a SIEM search, database query or investigation plan. A system can also draft containment steps or a case update. Query execution, account changes and other consequential actions should require authorization rather than occur because a model generated plausible text.
3. Fraud and account-abuse operations
PayPal’s explanation of its fraud program involved production data and feedback from internal agents and customers. A representative workflow is:
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- Collect transaction, account, device, identity, behavioral and network signals.
- Score unusual activity with specialized models, rules, anomaly detection or graph analysis.
- Route cases to approval, step-up verification, manual review or blocking.
- Feed confirmed outcomes and customer or investigator feedback into development.
- Retrain, validate and monitor for drift as attack patterns change.
Generative AI may help explain a score, organize evidence or create controlled test cases. It is not automatically superior at the underlying numerical classification task.
4. Protecting the customer experience
Fraud prevention is a balancing problem. More aggressive blocking can reduce losses while increasing legitimate declines; additional authentication can stop account takeover while making checkout harder. Useful measures include fraud loss, false positives, false negatives, legitimate approval rate, step-up rate, account-takeover rate, chargebacks, analyst handling time, response time, complaints and appeals. Results should also be examined by geography, product, payment method and customer segment.
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5. Internal security productivity
Security teams can use a governed assistant to find policies, compare incidents, draft playbooks and explain control requirements. This can reduce operational friction without giving the model authority to disable accounts, change production access or block payments.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Risks Keren highlighted—and what they require
The interview specifically called for scrutiny of data quality, intellectual property, security, privacy and compliance, along with testing for prompt injection, bias and hallucinations.
| Risk | Practical failure | Control to require |
|---|---|---|
| Prompt injection | Instructions hidden in an email, ticket, log or web page manipulate the model into ignoring its task or exposing data. | Treat retrieved content as untrusted, isolate tools, restrict permissions and red-team hostile inputs. |
| Hallucination | A fluent but incorrect incident explanation, query or remediation step misleads an analyst. | Require evidence links, human review, confidence signaling and tests against known cases. |
| Sensitive-data leakage | Payment, identity, source-code or incident data reaches an unauthorized model, log, plug-in or user. | Use data classification, least privilege, encryption, retention limits, tenant isolation and clear training-use terms. |
| Bias and uneven performance | Results differ across languages, regions, devices or customer groups. | Measure outcomes by segment and investigate disparate error rates before expanding use. |
| Data poisoning | Malicious or low-quality training or retrieval material changes recommendations. | Version sources, validate labels, restrict write access and monitor data quality. |
| Model drift | A model trained on old fraud patterns weakens after a new campaign appears. | Use ongoing benchmarks, drift alerts, retraining procedures and rollback plans. |
| Excessive autonomy | A bad recommendation becomes an outage, wrongful account closure or blocked payment. | Set approval gates, segregate duties, limit actions and provide emergency shutdown and fallback paths. |
How to validate a security-AI system
- Define the task. Separate classification, summarization, retrieval, recommendation and action. “Accuracy” has no meaning without a specific task, threshold, population and time period.
- Build a representative test set. Include historical incidents, recent attack examples, rare events, incomplete records, contradictory inputs and multilingual material.
- Measure the right errors. For classification, report precision, recall, false-positive and false-negative rates. For analyst assistance, compare recommendations with expert decisions and measure handling time and rework.
- Red-team the workflow. Test prompt injection, data exfiltration, poisoned retrieval content, unauthorized searches and attempts to trigger external tools.
- Verify evidence. Check whether conclusions are supported by retrieved records and whether the same input can be audited later through stored prompts, evidence, outputs, approvals and actions.
- Test degradation. Simulate provider outages, missing telemetry, rate limits, high alert volumes and stale indexes. A safe conventional process must continue when the model is unavailable.
- Gate high-impact actions. Require human approval for payment blocking, account suspension, identity decisions, production access changes and other irreversible outcomes.
- Monitor after launch. Track drift, segment-level performance, customer friction, security incidents, overrides and model or prompt changes.
Where GenAI is a poor fit
- Autonomous payment blocking or account closure without an appeal and rollback path.
- Identity, credit or lending decisions where opaque errors create legal or regulatory exposure.
- Workflows that cannot keep customer and payment data out of unauthorized prompts or retrieval indexes.
- Teams without enough telemetry, security expertise or staffing to verify outputs.
- Projects expecting a generic LLM to reproduce PayPal’s proprietary data, transaction scale and fraud operation.
What enterprise buyers should examine
| Evaluation area | Questions to ask |
|---|---|
| Data controls | Where is data processed and stored? What are retention, deletion, residency and model-training terms? Who can administer the environment? |
| Evidence | Can the product show retrieved records, citations, confidence indicators and reproducible audit logs? |
| Access and integration | Does it connect to the SIEM, SOAR, EDR, identity, fraud and case systems with least-privilege permissions? |
| Automation boundaries | Which actions require approval? Are rollback, emergency shutdown and safe fallback built in? |
| Evaluation | Are prompt-injection tests, regression suites, drift monitoring and segment analysis available? |
| Operations and economics | What are the latency, availability, rate limits, data-volume charges, minimum commitments, support terms and exit options? |
Security copilots such as Microsoft Security Copilot, Google Security Operations, CrowdStrike Charlotte AI and Splunk’s AI capabilities may suit teams already invested in those ecosystems. Cloud platforms such as Amazon Bedrock and Google Vertex AI are options for building a governed internal workflow, but require engineering and model-risk ownership. Fraud-focused providers such as Sift and Featurespace address transaction and account abuse more directly than a general SOC assistant. None should be assumed to replicate PayPal’s proprietary operation, and current pricing and availability require confirmation from each vendor.
What remains unknown about PayPal’s 2023 account
The interview did not disclose a production GenAI architecture, model provider, evaluation methodology, false-positive or false-negative rates, approval rates, latency targets, data-segmentation design or human-review process. It also does not establish that the executives, account totals, data volumes or practices described then remain current in 2026. Those are questions a buyer should put directly to PayPal or any vendor before relying on comparable claims.
The practical lesson
PayPal’s lesson is not to put an LLM in charge of fraud. It is to embed AI in a mature security and risk operation: use specialized models for structured, latency-sensitive decisions; use generative systems to help people investigate and explain; protect sensitive data; test adversarially; measure customer impact; and keep consequential actions reviewable and reversible.
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