Phishing Simulation False Positives: How to Improve Data Quality

A phishing simulation shows that several users clicked a test link within seconds of delivery. Did those employees really open the email and click? Possibly—but some events may have been generated by email security systems scanning links before the messages reached users.

When an automated event is mistaken for human behaviour, employees can be unfairly labelled as vulnerable, remediation training can be misdirected and leadership reports become difficult to trust. This makes measurement quality a critical part of any simulation programme.

What is a false positive in phishing simulation?

A false positive occurs when a simulation records an action or outcome that did not actually happen as attributed. For example, an automated security scanner may request a tracked URL, while a dashboard incorrectly counts that request as a human click.

A related measurement issue affects email-open events. Open tracking often depends on a remote image or pixel. Mail clients, image proxies and security services can preload or block images, making open counts imperfect indicators of employee attention.

Common sources of misleading events

Link inspection by security tools. Gateways may visit suspicious-looking URLs to assess them before delivery or shortly after receipt.

Image prefetching or proxying. A security system or email client may request tracking images automatically. Conversely, blocked images may mean a genuine open is not counted.

Automated browsing. Sandboxes can follow redirects and load pages in ways that resemble interaction.

Network attribution gaps. Shared IP addresses, web proxies and browser privacy features can limit the ability to connect a request to a specific person.

Overly broad event definitions. Counting every URL request as a confirmed employee action mixes technical telemetry with behavioural evidence.

No single technical signature reliably distinguishes every scanner from every person, so rules should be validated against the organisation's mail environment.

Five steps to improve reporting quality

1. Establish a clear event taxonomy

Distinguish email sent, delivered, pixel requested, link requested, verified user interaction, simulated credential submission and employee report. Avoid presenting them as interchangeable measures.

2. Review unusually fast activity

A large burst of near-instant link requests or an implausibly high campaign-wide click rate may indicate automated scanning. Treat these patterns as investigation signals, not automatic proof.

3. Test against the actual mail gateway

Run controlled test deliveries through the customer's approved infrastructure, including relevant secure email gateways and mail clients. Document what each system does to links and pixels under the configured policies.

4. Use corroborating signals where appropriate

Evaluate multiple permitted signals before classifying a high-impact outcome. Be particularly careful about attributing sensitive simulated actions to an individual; preserve privacy and avoid collecting real credentials.

5. Make uncertainty visible in reports

Flag unverified or scanner-suspected events and explain any exclusions. Reconcile anomalies before calculating employee risk or distributing mandatory remedial training.

The metrics leadership should see

Provide a breakdown of delivery, employee reporting, validated link interactions, completion of follow-up learning and trends by department. Add time to report and scenario difficulty where relevant. Explain the confidence level of behavioural measures and any changes in event definitions between reporting periods.

PhishSheriff's Human Risk Score methodology highlights why interpreting underlying signals matters when describing a user's risk. The board reporting guide offers complementary advice on presenting measures with context.

Better data produces fairer training

Do not assign corrective learning solely because a tracking pixel fired or a link was requested by a scanner. Instead, investigate the event and use trustworthy signals to identify the behaviour that genuinely needs reinforcement.

A phishing simulation is valuable only if the data can support a reasonable next action. Accuracy, transparent methodology and a fair experience for employees are essential for a long-term programme.

Frequently asked questions

Can email gateways click phishing simulation links automatically?

Yes. Link inspection and detonation workflows can generate requests without employee interaction.

Are email-open rates reliable?

They are useful as rough indicators in some contexts but can be affected by image blocking, prefetching and proxy services.

Should every click change a Human Risk Score?

High-impact conclusions should depend on validated signals and a documented scoring approach, not blindly on every raw HTTP request.