What Should US Based Enterprises Assess When Choosing the Best BEC Solution for the AI Era
AI-driven Business Email Compromise (BEC) attacks are rising in the U.S., heightening financial loss risks. These attacks target executives and finance teams.
Josh Bass CPO · · 7 min read
Business Email Compromise (BEC) is a sophisticated social engineering scam that defrauds companies by tricking employees into making unauthorized payments or disclosing sensitive data. Attackers often impersonate trusted individuals (e.g. executives, vendors, or lawyers) and conduct detailed reconnaissance of your organization beforehand. A few common BEC scenarios:
- Vendor Invoice Fraud: Criminals spoof or compromise a vendor’s email to send fake invoices or payment instructions. For example, fraudsters can “hack” a supplier’s mailbox or use lookalike domains to redirect payments to their accounts.
- Executive (CEO/CFO) Fraud: Scammers pose as company leadership (often called “whaling”), sending urgent wire-transfer requests. Messages frequently invoke mergers or deadlines to push finance staff to act quickly.
- Account Takeover: An attacker steals credentials for a real internal account (e.g. by phishing) and then sends financial requests from that legitimate email. Because the mail comes from a trusted employee, it can bypass basic filters.
- Attorney/Legal Impersonation: Attackers masquerade as lawyers or consultants involved in confidential business matters, pressing employees to send money under secrecy clauses. These often cite legal emergencies or use seemingly official documents.
- Payroll and Gift Card Scams: Examples include fake HR emails requesting employee payroll changes or executive emails instructing staff to buy gift cards. The FBI’s BEC guidance even cites scenarios like a CEO asking for hundreds of gift cards with a promise to distribute them internally.
Notably, BEC emails typically contain no malware or malicious attachments. They are text-only, highly targeted, and crafted to look ordinary. Because they often originate from legitimate domains (sometimes even compromised accounts), they may pass standard email authentication. This low-volume, high-context attack style is why traditional email filters alone often fail to catch BEC.
The Rise of AI-Driven BEC
The AI era has accelerated BEC. The FBI’s 2024 Internet Crime Report found BEC accounted for $2.77B in losses, making it the second-largest loss category after investment fraud. Industry analysts report that BEC attack volumes are rising – one source noted a ~42% increase in BEC campaigns in early 2024 vs. the prior year. Remote work and abundant online information give attackers more targets to impersonate, and generative AI tools allow them to craft believable lures at scale.
Given this surge, email security must evolve. NIST’s Trustworthy Email guidelines recommend SPF/DKIM/DMARC for domain authentication, but these alone do not ensure message authenticity or sender intent. Indeed, advanced BEC emails often pass DMARC checks by using legitimate or lookalike domains. Defense requires adding content and behavior analysis layers on top of basic auth. Attackers today insert themselves into normal workflows and communication threads, so solutions must understand context, not just rules.
How AI-Powered BEC Detection Works
Modern BEC solutions layer multiple analyses (often AI/ML-driven) to catch anomalies that static filters miss:
- Email Authentication & Metadata Checks: First, the system examines SPF/DKIM/DMARC and routing headers. Legitimate spoof attempts (e.g. domain or display-name spoofing) can trigger an alert. However, sophisticated attackers may use a genuine account or subtle domain lookalikes that still pass these checks, so domain auth is only an initial filter. NIST notes that SPF/DKIM/DMARC should be enabled, but also recommends complementary measures (TLS, S/MIME, strict mail policies) to truly secure email.
- Natural Language Processing (NLP) and Content Analysis: The system scans email text for signs of fraud. Advanced tools use AI models to detect unusual language cues – e.g. an urgent money request that doesn’t match the sender’s historical style. They flag phrases like “ASAP”, “wire today”, or grammar patterns that deviate from the user’s norm. Some vendors now specifically advertise that their ML can even spot AI-generated text patterns. The goal is to recognize the intent of a message, not just its surface content.
- Relationship and Behavioral Profiling: The platform builds a dynamic map of normal communication flows. It learns “who emails whom, when, and about what” over time. If an email falls outside these patterns – for example, a new requester, an unusual time or location, or an unexpected approval chain – it raises a red flag. For instance, an engine might note: “This is the first time CFO Bob has ever emailed contractor Alice with a transfer request,” which would be anomalous.
- Threat Intelligence and Domain Hunting: Many solutions proactively scan the web for threat indicators like typosquatted domains, fake login pages, or new phishing kits. By identifying malicious infrastructure in advance, they can block related emails immediately when they appear in your inbox.
- Automated Response & Workflows: Once an email is flagged as suspicious, the system can quarantine it, delete it, or require secondary approval. Some platforms can even roll back sent emails. Alerts are typically sent to security teams for investigation.
Deployment and Integration Considerations
Before selecting a solution, ensure it fits your environment:
- Platform Support: Verify compatibility with your email systems. For Microsoft 365, solutions may integrate via Microsoft Graph APIs or as a Secure Email Gateway. API-based connectors (post-delivery) avoid rerouting mail but require admin consent; gateway systems need MX record changes but filter mail in transit. Similarly, ensure full support for Google Workspace accounts via Gmail APIs or SMTP connectors. If you have on-prem Exchange, confirm hybrid or on-prem options.
- Integration Points: Check how the product integrates with your security stack. It should export alerts and logs to your SIEM/SOAR (via common formats like CEF/Syslog or native apps) and support hooking into incident workflows (ticketing, chat notifications, etc.). Also look for pre-built connectors to identity providers (Azure AD, Okta) and DLP tools.
- Identity and Endpoint Signals: The best solutions correlate email events with identity/endpoint data. For example, an unusual login (MFA bypass attempt) combined with a large payment request is a strong compromise indicator. Ask if the vendor ingests telemetry from identity management or endpoint security tools.
- Data Residency & Compliance: Determine where email content is processed and stored. U.S. enterprises often require data to reside in approved regions. Verify that the vendor’s cloud meets your compliance needs (SOC 2, ISO27001, HIPAA, etc.). For regulated sectors (finance, healthcare), ensure adherence to relevant rules (e.g. GDPR, CCPA).
- Scalability & Reliability: Large organizations should test performance under real load. Check the vendor’s SLAs for uptime and latency. Ask about multi-tenancy vs. dedicated instances if you have very high volumes.
- Administration and Usability: Consider who will manage the product. Is there a centralized console? Role-based access? Easy policy configuration? A solution that’s intuitive to operate will save your team time.
In summary, make sure the solution works seamlessly with your existing tools. A joint architecture review with the vendor can uncover any hidden integration issues or prerequisites early on.
10-Point Vendor Evaluation Checklist
Use this checklist to compare products:
- Impersonation Detection: Does the solution flag lookalike domains and spoofed sender names? Verify it catches common tricks (typos, homoglyphs) and rejects unauthorized “from” addresses.
- Compromised Account Identification: Can it detect when a legitimate mailbox is hijacked? Look for behavior-based alerts (e.g. an executive’s account suddenly sending unusual payment requests) and integration with login/activity logs.
- Conversation & Relationship Analysis: Does it analyze email threads and contact graphs? Check if the tool can spot an email that breaks the normal communication flow (e.g. a new, unexpected recipient).
- Language/NLP Scoring: Verify it uses AI to analyze message content. Test whether it flags messages with urgency cues, unusual phrasing, or style inconsistencies.
- Financial Data Extraction: Does it recognize and compare invoice fields? The product should detect changes to account numbers, routing details, or payee information.
- Machine Learning/AI Efficacy: Inquire about the ML models. Can they identify AI-generated or heavily disguised text? Request any published accuracy data.
- Automated Remediation: Evaluate built-in responses. Can it automatically quarantine suspicious emails, alert teams, or even simulate response actions?
- Integration & Deployment: Ensure it supports your mail platforms (M365, Gmail) and fits your architecture (API vs gateway). Look for SIEM/SOAR connectors and single-sign-on support.
- Reporting & Forensics: Check if the solution provides clear dashboards and detailed incident logs. It should allow you to audit what happened with each flagged email.
- Tunability & False Positives: How easy is it to adjust policies? Can you whitelist safe exceptions or retrain the system? A good product minimizes false alarms and learns from feedback.
Proof-of-Concept (POC) Test Plan
Conduct a hands-on POC with realistic test emails and clear success criteria. For example:
- Duration & Phases: Schedule 2–4 weeks. Week 1: integrate the solution in monitoring mode (no production impact) and collect baseline metrics. Week 2: run test scenarios. Week 3: analyze results and meet with stakeholders.
- Test Scenarios: Craft malicious and benign emails reflecting your business context. Example tests:
- Success Metrics: Define pass/fail thresholds. For instance:
- Detection Rate: ≥90% of malicious tests caught.
- False Positives: <5% of benign emails flagged.
- Response Time: Alerts generated within a few minutes.
- Usability: Admins can quickly identify and adjust to flagged cases.
- POC Schedule: An illustrative timeline:
- Acceptance Criteria: Verify that all high-risk tests were caught or quarantined, legitimate mail flow was undisturbed, and the solution’s alerts & reports behaved as expected. If these criteria are met, the solution passes the POC.
Final Thoughts
Protecting against BEC in 2026 requires a blend of advanced technology and sound processes. Alongside choosing a capable solution, enforce baseline defenses (enable multi-factor authentication, train staff to question unusual requests, implement multi-channel payment verification).
Consider using StrongestLayer’s Email Posture Check tool to identify if attackers can easily profile your organization. Explore our Threat Lab for research on evolving email attack techniques and defenses. Finally, schedule security exercises (e.g. phishing drills) to keep your teams alert to BEC indicators.
By rigorously applying this framework and leveraging available resources, you can select a BEC protection solution that safeguards your enterprise’s finances and data in the AI era.
FAQ
How is BEC different from phishing?
Phishing commonly uses deceptive messages to trick people into clicking links, opening attachments, or revealing credentials. BEC often focuses on convincing someone to take a business action, such as paying a fraudulent invoice or changing a supplier’s bank details. The two can overlap: a phishing attack may provide the credentials an attacker later uses to carry out BEC.
Can BEC emails pass SPF, DKIM, and DMARC checks?
Yes. SPF, DKIM, and DMARC help verify whether email is authorized to use a domain, but they do not prove that a message’s request is legitimate. A BEC email sent from a compromised, legitimate account may pass authentication checks. These controls are important, but organizations also need to evaluate sender behavior, message context, and unusual requests. See NIST’s guidance on trustworthy email.
How does AI help detect BEC attacks?
AI-assisted email security can analyze message content, sender-recipient relationships, communication patterns, and signs of impersonation to identify suspicious behavior. For example, an unexpected request to change payment details may be flagged even when the email contains no suspicious link. Detection quality depends on the signals available, the system’s configuration, and how well it fits an organization’s normal workflows.
Can AI-generated BEC emails be detected reliably?
Not by identifying AI-written text alone. Generative AI can produce polished messages that resemble ordinary business communication, making grammar or writing style less dependable as warning signs. More useful detection combines content analysis with identity, sender history, conversation context, and the risk of the requested action. No single indicator or detection method can guarantee that every BEC attempt will be caught.
What should a BEC protection solution detect?
A BEC protection solution should be evaluated on its ability to identify executive and supplier impersonation, lookalike domains, suspicious requests for payments or sensitive data, compromised-account activity, unusual changes to financial details, and unexpected behavior within existing email conversations. Buyers should also assess alert quality, investigation context, response options, and the volume of legitimate messages incorrectly flagged.
How should an organization evaluate BEC protection?
Start by mapping the organization’s main risks, such as supplier payments, payroll changes, executive requests, and access to sensitive data. Then assess how a solution integrates with the existing email environment, what signals it analyzes, how it explains alerts, and how administrators investigate incidents. Validate the claims with realistic scenarios and measure both threat detection and disruption to legitimate email.
What should a BEC protection proof of concept measure?
A proof of concept should test realistic malicious and legitimate messages, including impersonation attempts, unusual payment requests, compromised-account scenarios, and benign messages that look unusual. Track which attacks are detected, which are missed, false positives, alert quality, investigation effort, and response time. Agree on success criteria before testing, and use representative data wherever possible rather than relying only on vendor-provided examples.
Does BEC protection replace MFA, email authentication, or payment verification?
No. BEC protection is one layer of a broader security strategy. Multi-factor authentication (MFA), strong email authentication, account monitoring, employee awareness, and clear payment-approval procedures address different parts of the risk. Independently verifying changes to bank details or urgent payment requests through a trusted, previously established channel remains important—even when an email appears to come from a familiar person.
What should an organization do if it suspects a BEC attack?
Do not reply to the suspicious request or use contact details supplied in the message to verify it. Contact the purported sender through a known, independent channel, and report the email to your security team. Preserve the message and relevant details for investigation. If money has been transferred, contact the bank or payment provider immediately. If an account may be compromised, follow incident-response procedures to secure it and investigate related activity.