Deception technology deploys decoy assets — fake servers, honeypot credentials, deceptive files, and fictitious accounts — that have no legitimate business purpose. Any interaction with deception assets is a high-fidelity indicator of compromise, delivering threat detection with near-zero false positives.
Deception technology is one of the highest signal-to-noise security investments available — any alert is genuine because legitimate users have no reason to interact with decoys. RLM advises on deception platform selection, deployment strategy, and integration with incident response workflows.
A structured advisory process — from security posture assessment and market evaluation to vendor selection, contract negotiation, and post-deployment validation — tailored to your risk profile and compliance obligations.
We define the deception strategy — which attack scenarios to detect (lateral movement, credential theft, ransomware staging), what types of decoys are most effective in your environment, and the coverage that maximizes detection value.
We evaluate deception platforms — Attivo Networks (SentinelOne), Illusive Networks, Cameleon Security, and honeypot infrastructure — against your environment, deployment complexity, and integration requirements.
Effective deception requires decoys that are indistinguishable from legitimate assets and placed in the paths that attackers traverse. We design the deception deployment — breadcrumb strategy, decoy placement, and the environment authenticity that makes decoys convincing.
Deception alerts are high-fidelity — they should trigger immediate, assertive response. We design the SOAR integration that automates initial containment and investigation actions upon deception detection.
These are the dimensions that consistently separate effective security programs from expensive ones — and the questions RLM will help you answer before any vendor commitment.
Deception only works if attackers believe decoys are real. Evaluate the platform's ability to create convincing decoys that match your environment — using real operating system images, authentic-looking credentials, and environment-specific naming conventions.
Breadcrumbs (fake credentials, fictitious share paths, deceptive tokens) planted on real systems guide attackers toward decoys. Evaluate breadcrumb generation quality and the deployment mechanism that plants them on real endpoints without impacting performance.
A primary advantage of deception is near-zero false positives — any alert means a real interaction with a decoy. Evaluate whether the platform maintains this advantage in your environment — particularly for automated processes that might trigger decoys.
Deception environments require ongoing maintenance as real infrastructure changes. Evaluate the operational overhead of keeping decoys current — authenticity degrades if decoys fall out of sync with the real environment.
Deception is excellent for lateral movement detection but covers fewer attack scenarios than SIEM or EDR. Evaluate deception as a complement to, not replacement for, other detection controls — specifically the attack phases where deception provides unique value.
Deception assets can be used as threat intelligence collection points. Evaluate the platform's ability to collect attacker TTPs, tools, and infrastructure data from decoy interactions — converting detections into actionable intelligence.
"RLM helped us build a security program that satisfied our board and our auditors — without locking us into a single vendor's roadmap. Their independence is the whole point."
"We had three overlapping security tools doing the same job. RLM helped us rationalize the stack, cut spend by 30%, and actually improve our detection coverage in the process."
Start with a no-cost conversation with an RLM security advisor — vendor neutral, no agenda, just clarity on where your gaps are and the right path to close them.
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