How to Benchmark Your Organisation’s AI Social Engineering Resilience

Yes, you can benchmark your organisation’s resilience to AI social engineering attacks, but the data requires a different approach than traditional phishing metrics. Unlike email phishing, where platforms like KnowBe4 have built industry benchmarks over years, AI social engineering resilience benchmarking is an emerging discipline.

This guide explains what AI social engineering benchmarking involves, what metrics matter, and how to establish baseline measurements for your organisation.

Why Traditional Phishing Benchmarks Don’t Transfer

Existing awareness training platforms provide benchmarks for email phishing susceptibility, including average click rates by industry, company size, and campaign type. According to KnowBe4’s industry benchmarking data, organisations can compare their click rates against peers and track improvement over time.

But these benchmarks measure a specific attack vector: text-based emails with malicious links or attachments. They do not measure:

• Susceptibility to voice clone calls impersonating executives or colleagues
• Response to deepfake video calls on Teams or Zoom
• Effectiveness of verification workflows under pressure
• Whether employees escalate suspicious AI-powered contacts

AI-powered attacks have fundamentally different characteristics. Research indicates that AI-generated phishing content achieves click rates of approximately 54% compared to 12% for manually crafted phishing, according to analysis reported by Harvard Business Review. Voice and video attacks add additional dimensions that email metrics cannot capture.

What AI Social Engineering Benchmarking Measures

Effective AI social engineering benchmarking examines multiple dimensions:

How Industry Benchmarking Works

Benchmarking requires aggregated data across multiple organisations. This data must be:

• Anonymised: Individual organisation results are never shared; only aggregate patterns by industry and size bracket
• Comparable: Testing methodology must be consistent across organisations to enable valid comparison
• Current: AI attack techniques evolve rapidly; benchmarks must reflect recent assessments

Organisations running AI social engineering assessments contribute to a growing dataset that enables peer comparison. After an assessment, you can see how your deepfake detection rates, voice clone susceptibility, and verification workflow effectiveness compare against similar organisations.

Establishing Your Benchmarking Baseline

Before you can benchmark against peers, you need internal baseline measurements. This typically involves:

• Initial assessment: A red team engagement testing AI social engineering resilience across relevant attack vectors
• Multi-dimensional scoring: Results across people, processes, technology, workflows, and awareness training effectiveness
• Risk quantification: Translating technical findings into business risk terms the board can understand

This baseline becomes your reference point for measuring improvement and for peer comparison.

What Boards Want to Know about AI Defence

When board members ask ‘How do we compare on AI threat defence?’, they are looking for:

• Concrete comparison against industry peers, not just internal metrics
• Trend data showing improvement (or regression) over time
• Evidence that security investments are producing measurable results
• Context for the risk level: are they above or below average exposure?

Traditional awareness training platforms cannot answer these questions for AI social engineering because they do not test these attack vectors. Benchmarking fills this gap.

Using Benchmarks for Remediation Planning

Benchmarking is not just about comparison. It informs where to focus remediation efforts. If your organisation scores below average on verification workflow effectiveness but above average on deepfake detection, that suggests:

• Employee awareness of synthetic media is reasonable
• Procedural controls need strengthening
• Training should emphasise callback procedures, not just threat recognition

This targeted approach is more effective than generic awareness training that treats all organisations the same way.

Frequently Asked Questions

Is AI social engineering benchmarking new?

Yes. Email phishing benchmarks have existed for over a decade, but AI social engineering benchmarking is an emerging discipline. The data requires organisations to run AI-specific assessments rather than traditional phishing campaigns.

How is benchmark data kept confidential?

Benchmark data is aggregated and anonymised. Only comparative metrics are shared. Details about specific organisations, their vulnerabilities, or their employees are never disclosed.

How often should we reassess?

Most organisations benefit from annual AI social engineering assessments, with more frequent testing (quarterly) for high-risk industries like financial services. This provides trend data for board reporting and ensures benchmarks reflect current resilience.

Can we benchmark internally first before comparing to peers?

Yes. Many organisations start with an internal baseline, implement remediation measures, then reassess to measure improvement. Peer benchmarking adds external context once you have internal data.

Breacher.ai is building the first industry benchmarks for AI social engineering resilience, providing security leaders with peer comparison data that did not previously exist.