Social engineering is responsible for the majority of enterprise breaches — and it's evolving faster than rule-based email security can track. AI-powered social engineering detection uses behavioral analysis, language models, and sender reputation intelligence to catch sophisticated phishing, BEC, and impersonation attacks at inbox scale.
Modern social engineering attacks are carefully crafted to evade traditional filters. AI detection looks beyond signatures and rules — analyzing message tone, sender behavior patterns, request context, and linguistic cues to identify manipulation attempts that no rule would catch.
Every engagement follows a structured process — from discovery and vendor evaluation to pilot design and scale — adapted to the specific constraints and maturity of your organization.
We evaluate your current email security posture — Microsoft Defender, Proofpoint, Mimecast, or others — and identify specific gaps in social engineering detection coverage where AI-powered augmentation would have the greatest impact.
We evaluate platforms like Abnormal Security, Darktrace Email, and Tessian against your specific threat profile, Microsoft 365 / Google Workspace environment, and integration requirements.
High-risk users — executives, finance team, IT admins — require different detection sensitivity than general population. We design user risk segmentation that applies appropriate detection levels without flooding analysts.
Detection technology works best when paired with a security-aware workforce. We advise on how to use AI detection data to target security awareness training to actual attack patterns hitting your organization.
These are the evaluation dimensions that consistently separate successful deployments from expensive pilots that never reach production scale.
Signature-based tools catch yesterday's attacks. Evaluate how the platform performs against newly crafted, never-seen-before social engineering attempts — typically validated through red team exercises.
BEC attacks use legitimate accounts to request fraudulent wire transfers or credential changes. Evaluate specific BEC detection capability, including lookalike domain identification and request context analysis.
Business email involves legitimate urgency, requests, and unusual communication patterns. Validate false positive rates on your actual email traffic before deployment — not on vendor-selected test sets.
Native integration with your email platform is essential. Evaluate API access depth, quarantine capabilities, and the ability to remediate across the full mailbox, not just incoming messages.
Speed of detection and quarantine matters when a BEC attack is in progress. Evaluate the end-to-end latency from email receipt to quarantine action.
Attack data should feed security awareness programs. Evaluate reporting capabilities that identify which attack types are targeting your organization and which user populations are most at risk.
"RLM brought structure to a process we didn't know how to start. They asked the right questions, surfaced the right vendors, and kept us from making decisions we would have regretted."
"What set RLM apart was that they didn't have a preferred answer. They evaluated our options honestly and told us what they actually thought."
Start with a no-cost conversation with an RLM AI advisor — vendor neutral, no agenda, just clarity.
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