Each of Andeavour's products is detecting, illuminating and preventing dangerous unseen trends across your organization before they become critical security incidents.
Identify new attack patterns and threat vectors before they become widespread security incidents
Example: Early detection of zero-day exploits and advanced persistent threats
Analyze historical data to predict potential security vulnerabilities and system weaknesses
Example: Predicting ransomware targets based on infrastructure patterns
Uncover non-obvious relationships between security events across different systems and timeframes
Example: Linking seemingly unrelated log entries to sophisticated attack campaigns
Collect security data from multiple sources and timeframes
Apply AI algorithms to identify subtle patterns and anomalies
Connect seemingly unrelated events to reveal dangerous trends
Provide actionable insights to prevent security incidents
Illuminating Dangerous Trends is the core capability that underlies every Andeavour product, from AgentGuard to the Agentic SOC. Rather than waiting for a single alert to cross a static threshold, this capability continuously watches how activity is changing shape over time across agents, users, and systems, and surfaces the trajectories that are moving toward a security incident before that incident occurs.
Most detection tooling is built to answer a narrow question: is this one event bad? Illuminating Dangerous Trends asks a broader question: is the pattern this event belongs to becoming more dangerous over the last hour, day, or week, and does it resemble the early stages of an attack technique documented in frameworks such as MITRE ATT&CK. That shift, from point-in-time alerting to trend-aware detection, is what lets security teams intervene while an attack is still forming rather than after damage has already occurred.
The capability ingests telemetry from agent activity logs, identity and access events, network flows, and application audit trails, then normalizes that data into a common timeline. From there it builds a rolling baseline of what normal behavior looks like for each entity, whether that entity is a human user, a service account, or an autonomous AI agent acting inside your environment.
Deviations from baseline are not treated as isolated alerts. Instead, they are grouped into candidate trends, sequences of related deviations that share an actor, a target, or a technique, and each trend is scored on how closely it tracks known attack progressions and how quickly it is accelerating.
Underneath, the system combines statistical anomaly detection for high-volume, low- context signals with transformer-based sequence models for the harder problem of recognizing multi-step behavior that unfolds over days or weeks. The sequence models are trained on labeled attack chains and validated against held-out incident data so that scoring reflects real-world attacker behavior rather than synthetic patterns.
Every scored trend carries a full evidence trail, the specific events, timestamps, and entities that contributed to the score, so analysts can validate a finding in seconds rather than re-deriving it from raw logs. This approach and its evaluation criteria are documented in detail in our methodology.
A service account slowly accumulates permissions across several unrelated change requests over a number of weeks. Individually, each change is approved and benign looking; taken together, the trend shows an account moving toward administrative access it has never used.
An autonomous AI agent begins invoking a data export tool at a slightly higher frequency each day. No single call is abnormal, but the trend line shows a consistent upward slope consistent with a slow data exfiltration pattern.
Low-volume scanning activity from several source IPs, individually below any alerting threshold, is correlated by timing and target overlap into a single coordinated reconnaissance trend.
Teams that adopt trend-based detection typically report earlier intervention points, fewer isolated false-positive alerts, and a meaningful reduction in the analyst time spent manually correlating events across systems. Outcomes vary by environment and are measured against the baselines documented in our research publications.
No. It sits alongside your existing log and telemetry infrastructure and adds trend-level correlation and scoring on top of the events you already collect.
Generic anomaly detection flags individual outliers. This capability groups related outliers over time into a single trend and scores that trend against known attack progressions, which is what reduces noise while catching slow-moving threats.
It underpins the Agentic SOC platform and is described in more detail on the what is an Agentic SOC page.
Don't wait for security incidents to happen. Illuminate dangerous trends before they impact your organization with our advanced AI-powered detection capabilities.