Phishing Simulation Reporting: 12 Features Security Teams Should Compare (Dashboards, Metrics, and Audit Evidence)

Phishing simulations live or die on reporting.

Not “pretty charts”—but reporting that helps you:

• prove improvement over time (without blaming individuals),
• identify where training needs to change,
• and produce evidence that stands up in security reviews and audits.

If you’re evaluating a phishing simulation / awareness training solution, this guide breaks down the phishing simulation reporting features that matter most to security engineers, IT admins, CISOs, and compliance teams.

You’ll also see what “good” looks like for each feature—so you can compare tools consistently.

What phishing simulation reporting is for (and what it’s not)

A mature phishing simulation program uses reporting to answer three questions:

1. Risk & behavior: Are users recognizing and reporting suspicious messages faster?
2. Program quality: Are we training the right things (by role, region, and threat pattern)?
3. Governance & evidence: Can we show what we ran, why we ran it, and what we changed based on results?

What reporting should not become:

• A “gotcha” leaderboard
• A punishment mechanism
• A vanity metric (e.g., obsessing over click rate without looking at reporting rate or follow-up training)

When reporting is used correctly, it becomes the link between security, IT operations, and compliance.

The 12 phishing simulation reporting features to compare

1) Clear campaign-level KPIs (not just user-level events)

At minimum, your reporting should make it easy to see—per campaign:

• delivery rate (sent vs delivered vs bounced)
• open rate (optional, depending on mail clients and privacy limitations)
• click rate
• report rate (how many users reported the simulation as suspicious)
• completion rate for follow-up training

Why it matters: campaign-level KPIs let you compare like-for-like runs (e.g., “invoice theme” in Q1 vs Q3) and avoid chasing noise.

2) Segmentation by role, department, and location (with guardrails)

Security teams need segmentation to tailor training:

• finance vs HR vs IT vs executives
• regions/languages
• office vs remote workers

But segmentation also creates privacy risk. Look for:

• configurable visibility (who can see what)
• aggregated views by default
• the ability to minimize or anonymize individual results where appropriate

If privacy is a priority in your org, review AutoPhish’s approach here: https://autophish.io/anonymization

3) Deliverability and email health reporting

Many programs “fail silently” due to deliverability:

• messages land in spam/quarantine
• domains or sending infrastructure get blocked
• tracking gets stripped

Strong reporting includes:

• bounce classification and trends
• domain/sender reputation signals (where available)
• per-recipient-domain results (e.g., Microsoft 365 vs Google Workspace)

Tip: before you blame users or templates, validate your DNS and alignment. AutoPhish provides a quick pre-check: https://autophish.io/dns-check

4) Evidence-friendly exports (PDF) with consistent definitions

Compliance and leadership reporting often requires offline artifacts.

Look for:

• PDF exports for board / audit packs
• consistent metric definitions across time (e.g., what exactly counts as a “click”)
• stable identifiers for campaigns and templates

If you can’t export consistently, you can’t show improvement credibly.

5) Trend views that support continuous improvement

A single campaign is a snapshot. Good reporting shows trends across:

• quarters and years
• role-based cohorts
• template themes (credential lure vs delivery notice vs document share)

Look for:

• rolling averages
• cohort comparisons
• seasonality-aware views (avoid comparing holiday periods to normal operations)

7) Training workflow reporting (what happens after the event)

A phishing simulation shouldn’t end at “clicked.”

Better programs track what happens next:

• auto-assigned micro-training
• completion monitoring
• repeated patterns (e.g., users repeatedly missing the same red flags)

If your platform includes a training experience, it should be reportable end-to-end. See: https://autophish.io/training-platform

8) Scoring and risk signals (careful, but useful)

Some tools create a “risk score.” This can be useful if:

• it’s transparent (how the score is calculated)
• it’s not used punitively
• it’s supported by behavior improvements and training completion

Avoid black-box scoring that can’t be explained to leadership or works councils.

9) Integration signals (ticketing, SIEM/SOAR, identity)

Even if you don’t integrate on day one, reporting should make integration plausible:

• webhook / API availability (for pulling campaign results)
• identity mapping (so you can segment accurately)
• evidence that the platform can fit your incident reporting workflow

Reporting that can’t leave the tool becomes a dead-end. Contact us to get API access for AutoPhish!

10) Privacy controls and retention reporting

Security awareness data can be sensitive.

Look for features that support responsible handling:

• configurable retention periods
• role-based access control
• aggregated-by-default reporting
• anonymization or data minimization options

If you need a privacy-first baseline, start here: https://autophish.io/anonymization

11) “Explainability” for non-security stakeholders

CISOs and compliance leaders often need to answer:

• What did we do?
• What changed?
• Is the program improving risk?

Reporting should include a narrative-ready view:

• campaign summary
• key changes since last campaign
• recommended next steps driven by results

This is one of the biggest gaps in DIY and early-stage programs.

12) Benchmarking (internal > external)

External benchmarks can be misleading because organizations differ in:

• industry threat profile
• mail security posture
• workforce composition

Prefer internal benchmarking:

• “Our reporting rate improved from X% to Y% after policy + training changes”
• “Time-to-report p90 dropped by Z days”

That’s defensible and actually actionable.

A practical scoring checklist (copy/paste)

Score each item 0–2 (0 = missing, 1 = partial, 2 = strong):

• Campaign KPIs include report rate + time-to-report
• Segmentation exists with access controls
• Deliverability reporting is usable (bounces, trends)
• Export formats support audits (CSV/PDF)
• Audit trail exists (changes + approvals)
• Trend views support comparisons over time
• Follow-up training reporting exists
• Risk/scoring is explainable (or absent by choice)
• Integration path exists (API/webhooks)
• Privacy + retention controls are configurable
• Executive summary view exists
• Internal benchmarking is easy

A tool with a lower click rate but a higher report rate, better governance, and better evidence is often the better real-world choice.

FAQ

What is the most important metric in phishing simulation reporting?

There isn’t a single metric, but report rate and time-to-report often correlate better with organizational resilience than click rate alone.

Should we track open rates?

Open rates can be unreliable due to mail client privacy protections and image blocking. Use them cautiously; focus on delivery, clicks (where measurable), reporting behavior, and training completion.

Can phishing simulation reporting help with ISO 27001, NIS2, or GDPR accountability?

Reporting can support evidence collection and continuous improvement, but it does not “make you compliant.” Compliance depends on your policies, governance, lawful basis, access controls, and how you operate the program.

How do we keep reporting from becoming a blame tool?

Default to aggregated reporting, keep access limited, focus on training outcomes, and avoid individual public rankings.

Next step

If you’re comparing platforms and want reporting that’s operationally useful, privacy-aware, and easy to turn into audit-ready evidence, AutoPhish is built for security teams that need a repeatable program.

• Explore the platform: https://autophish.io/about
• See pricing: https://autophish.io/pricing
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