When AI Overselling Backfires
While I witness a European cybersecurity vendor saw a drop in proof-of-concept conversions after prospects flagged “AI-washing” in their messaging.
The lesson?
Idk, timing?
With CrowdStrike adding generative AI to Falcon and Microsoft baking Copilot into Defender, customers face a paradox: they want AI-powered efficiency but distrust vendors who overpromise.
The McKinsey AI 2023 report shows security teams prioritise AI tools that reduce mean time to detect (MTTD) by ≥40%, not buzzwords. Meanwhile, CISOs now benchmark AI claims against NIST’s AI Risk Management Framework before procurement.
To do;
Anchor in Existing Workflows
Example: Instead of “revolutionary AI,” frame machine learning (ML) enhancements to existing IAM (identity and access management) as “25% faster privilege escalation alerts via behaviour-based thresholds.” Cite your false-positive rate (aim for ≤5%) and how it compares to rules-based systems.
Quantify the “Before” State
Buyers need context. If your AI-powered NDR (network detection and response) tool reduces alert fatigue, state the industry baseline: “SOC teams waste 55% of time on false positives – we cut that by 60%.”
Demystify the Black Box
Technical buyers will probe your AI’s explainability. Prepare simple analogies: “Our EDR’s (endpoint detection and response) anomaly detection works like a credit card fraud alert – it flags deviations from 12+ behavioural baselines, not just signatures.”
Actionable Recommendations
- Benchmark against human labour: “Our generative AI drafts 80% of SOC-2 reports, saving 15 analyst hours per audit cycle”
- Show cost displacement: “Customers replace $280k/yr in manual threat hunting with our automated triage”
- Preempt regulatory concerns: Map training data to GDPR/CCPA requirements upfront
Question Worth Answering
When buyers can’t tell AI from AI-washing, the winning pitch isn’t about technology – it’s about trust. Ask yourself: would your claims pass a CISO’s “sniff test” if stripped of the word “AI”?
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