Behavioral Detections for Modern Enterprise Threats
A detection that doesn’t fire when it should is worse than no detection at all — it creates false confidence. Our Detection Engineering team builds, validates, and continuously maintains high-fidelity behavioral detections across identity, cloud, endpoint, SaaS, network, and AI systems. Every detection ships to Nexus customers automatically.
Detection Coverage Domains
Detection engineering at LogicBounce is a continuous, research-driven process — not a periodic project. New detections are developed, validated against real attack data, and shipped to Nexus customers every week.
Every detection starts with intelligence from the Threat Defense Unit, Identity Threat Research team, and AI & Agent Security Research team. We don’t build detections speculatively — we build them against documented, real-world attack techniques that have been observed in the wild or discovered through original research. This ensures every detection has validated attacker behavior behind it, not theoretical assumptions.
Once a technique is documented, we map the observable artifacts it leaves across telemetry sources — identity logs, cloud audit events, endpoint telemetry, SaaS activity logs, network flows, and AI agent behavioral data. We identify which data sources are required, which combinations of signals create high-confidence indicators, and how to normalize data from heterogeneous sources into a consistent detection model.
Detection logic is written as behavioral rules rather than signature-based patterns — detecting the actions, sequences, and anomalies that characterize attacker behavior rather than specific IOCs that change with every campaign. Detections are developed using graph analytics, time-series analysis, statistical baselining, and contextual enrichment from Atlas’s Security Graph to reduce false positives.
Every detection is validated against real attack simulation before shipping. We run adversary emulation exercises using the actual TTP being detected — confirming the detection fires correctly, measuring detection latency, testing edge cases, and tuning thresholds to hit our false positive target of less than 2%. Detections that fail validation go back to development, not into production.
Adversaries adapt. A detection that works today may degrade as attackers modify their techniques to evade it. Our detection maintenance program continuously monitors production detection performance, tracks adversary technique evolution, and updates detection logic when drift is detected — ensuring Nexus customers always have current, high-fidelity coverage.
Our detection library spans every major enterprise attack surface — with particular depth in the areas where modern attackers concentrate their efforts.
AiTM phishing, credential stuffing, token theft and replay, MFA bypass techniques, OAuth abuse, privilege escalation via identity systems, account takeover behavioral patterns, and impossible travel anomalies.
IAM privilege escalation, unusual resource provisioning, cloud storage exfiltration patterns, cross-account role assumption abuse, serverless function exploitation, and anomalous API call sequences.
Living-off-the-land binary abuse, process injection techniques, credential dumping from memory, lateral movement tool execution, defense evasion patterns, and ransomware pre-deployment behavioral indicators.
Abnormal data export patterns, OAuth application abuse, sharing permission escalation, mass download events, cross-SaaS lateral movement indicators, and third-party integration anomalies.
Prompt injection execution indicators, anomalous tool invocation patterns, agent behavioral drift, MCP permission abuse, unexpected API call sequences from agent processes, and agent-to-agent trust exploitation.
Command-and-control communication patterns, DNS tunneling, beaconing behavior, lateral movement via network protocols, data exfiltration volume anomalies, and unusual outbound connection patterns.
A sample of recently shipped detections. The full library of 500+ production detections is available to Nexus platform customers via the detection catalogue.
Detects cases where OAuth refresh tokens issued to third-party applications remain valid and active following a user password reset event — a common persistence mechanism used by attackers after initial credential compromise.
Behavioral detection identifying multi-hop IAM role assumption sequences that traverse organizational account boundaries in patterns consistent with privilege escalation rather than legitimate cross-account operations.
Detects AI agent tool call sequences that deviate significantly from established behavioral baselines — flagging patterns consistent with prompt injection execution where an agent performs atypical actions outside its normal operational scope.
Identifies authentication attempts against Entra ID-protected resources using legacy protocols (Basic Auth, NTLM) that bypass modern conditional access policies — commonly used to circumvent MFA requirements on accounts with legacy protocol access enabled.
Correlates large-volume file download events with subsequent sharing permission escalation within SaaS platforms — a behavioral pattern observed in both data exfiltration pre-departure scenarios and active insider threat cases.
Detects credential dumping attempts targeting LSASS using indirect syscall techniques designed to evade userland API hooking — a technique increasingly used by modern ransomware groups and nation-state actors to bypass EDR credential theft protections.
Detection Engineering translates output from every research area — Autonomous Defense Research and Threat Intelligence — into production detections that ship to every Nexus customer.
Our Detection Engineering team can assess your current detection coverage, identify gaps against the threats targeting your industry, and show you exactly how Nexus fills them.
