Beelzebub
Open-source-born AI honeypot framework that emulates systems to deceive, detect, and analyze attacker behavior at machine speed.
Visit Website ↗ + Add to CompareOverview
Beelzebub began in 2021 as an open-source honeypot project created by Italian security researcher Mario Candela, and was formally incorporated as a company (with co-founders Alessandro Risaro and Pierpaolo D’Odorico) around 2025, based in Milan. What started as a low-code deception framework has grown into a three-product platform: Arcangelo (continuous attack-surface mapping and exploit-path validation via agentic pentesting), the core Beelzebub Platform (AI-driven honeypots and canary credentials), and Caronte (malware/artifact analysis via the open-source Azazel sandbox).
Its differentiator is an open-source pedigree spanning over three years of community development, 2,000+ GitHub stars, and reported use by engineers at large organizations including Microsoft, Google, Cisco, and Red Hat, now being commercialized into an integrated active-defense loop that plugs into existing SIEM/XDR stacks. It remains a very small, early-stage company; no funding round was found in public records.
Innovation Matrix Assessment
Steady multi-year open-source evolution now being productized into a broader platform.
Useful deception/threat-intel capability, but a narrow addition to a mature SOC's toolset.
Strong open-source adoption metrics, but no disclosed funding and unproven commercial traction.
AI-enhanced deception improves on established honeypot tech rather than replacing the category.
Real GitHub install/star numbers are credible evidence, but enterprise usage claims are not the same as verified contracts.
Deception technology has durable but niche relevance in SOC toolkits.
Why CISOs Should Care
Adds low-cost, AI-driven deception and attacker-behavior intelligence to a SOC's detection stack, backed by a credible open-source track record.
What Makes It Different
Grew from a community-vetted open-source honeypot rather than a closed vendor product, then layered AI emulation and analysis on top.
The Matrix Verdict
48/100 — EMERGING / UNRANKED
A legitimate, technically credible early-stage vendor whose open-source traction outpaces its commercial proof points.
Editorial Note: Claims vs. Verified Findings
The claim of having "the trust of engineers at Fortune 500 companies including Microsoft, Google, Cisco, and Red Hat" refers to open-source usage, not confirmed enterprise contracts.
Sources
- Beelzebub — Active Defense for Machine-Speed Attacks — https://beelzebub.ai/
- Beelzebub: Open-source honeypot framework (Help Net Security) — https://www.helpnetsecurity.com/2025/02/10/beelzebub-open-source-honeypot-framework/
- GitHub - beelzebub-labs/beelzebub — https://github.com/beelzebub-labs/beelzebub
Alternatives to Beelzebub
Abnormal AI
AI-native behavioral security platform that analyzes sender identity and communication patterns, rather than message content alone, to stop…
Palo Alto Networks Cortex XSIAM
Palo Alto Networks' AI-driven 'autonomous SOC' platform that unifies SIEM, EDR, SOAR, and attack-surface data into a single…
Fenix24
Chattanooga-based ransomware recovery specialist that has restored operations after 500+ real-world incidents, including 30 Fortune 500 companies.
Torq
AI-native hyperautomation platform positioning itself as an 'agentic SOC,' using a multi-agent system to autonomously execute large volumes…
Lumu Technologies
Network detection and response vendor using Continuous Compromise Assessment to show where compromise has actually happened, integrating with…
Google Security Operations
Google's cloud-scale SIEM/SOAR (formerly Chronicle), unifying a petabyte-scale data lake with Mandiant frontline threat intelligence and Gemini-powered investigation.