Key Takeaways
- Many high‑risk alerts—such as WAF anomalies, supply‑chain warnings, dark‑web access listings, and cross‑domain authentication patterns—are never triaged by in‑house SOCs or MSSPs.
- Conventional AI‑SOC platforms are tuned for routine, high‑volume detections and leave these edge cases as “exceptions.”
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Introduction: The Persistent Alert Blind Spot
Security teams routinely face a flood of alerts, yet a significant subset never reaches the triage stage. These include Web Application Firewall anomalies that hint at subtle bypass attempts, supply‑chain warnings that surface only after a component is compromised, dark‑web access listings that reveal credential leaks, and cross‑domain authentication patterns that look innocuous until three disparate systems are correlated. Because each signal is low‑volume or context‑dependent, they fall outside the routine workflows of most SOCs and are often labeled as “noise” or left to languish in queues. The result is a persistent blind spot where genuine threats can dwell unnoticed, increasing the organization’s exposure to breaches that could have been stopped early.
Why In‑House SOCs and MSSPs Struggle
Internal security operations centers are constrained by analyst bandwidth, expertise, and the sheer volume of routine alerts that demand immediate attention. When an unusual pattern emerges—such as a rare authentication spike across cloud and on‑premises resources—SOC analysts may lack the contextual data or time to investigate thoroughly. Managed Security Service Providers (MSSPs) often escalate these ambiguous alerts back to the customer, creating a feedback loop that adds delay without resolution. Consequently, these edge alerts accumulate, treated as low‑priority exceptions rather than actionable intelligence, and the organization’s risk posture remains incomplete.
Shortcomings of Conventional AI‑SOC Tools
Most AI‑driven SOC solutions on the market today are built to optimize detection fidelity for high‑frequency, well‑understood signatures—think malware hashes, known exploit kits, or brute‑force login attempts. Their models are trained on large, homogeneous datasets, which makes them excel at filtering noise but poor at reasoning about sparse, multi‑domain signals that lack a clear precedent. When faced with a WAF anomaly that does not match any known rule set, or a dark‑web mention that requires threat‑intel correlation, these tools either generate false positives, ignore the alert, or flag it for manual review that never occurs. The net effect is a coverage gap precisely where sophisticated attackers operate.
Radiant Security’s Unique Positioning
Radiant Security claims to be the only AI‑SOC platform capable of triaging the high‑risk alerts that other solutions consistently miss. Rather than augmenting existing rule‑based engines with a thin layer of machine learning, Radiant was designed from the ground up as an agentic AI system that can autonomously investigate any alert, regardless of its source, volume, or complexity. This positioning stems from the founders’ firsthand experience with alert fatigue and their conviction that the core deficiency lies not in analyst skill but in the tools themselves. By focusing on the “uncovered” alerts, Radiant aims to close the blind spots that leave organizations vulnerable.
Agentic AI: Following Any Alert Across Any Domain
At the heart of Radiant’s technology is an agentic AI architecture that treats each alert as a starting point for an autonomous investigation. The system can pull data from network logs, endpoint telemetry, cloud activity feeds, threat‑intel repositories, dark‑web monitors, and identity platforms without needing predefined playbooks for each combination. Because the AI reasons over the relationships between these data points, it can follow a trail that begins with a subtle WAF anomaly, moves through unusual API calls, and ends with a credential leak discovered on a dark‑web forum—all within a single, continuous investigation. Importantly, there is no artificial ceiling on the number of domains or data sources the AI can encompass; its coverage expands as the environment grows.
Reasoning Through Exceptions
Traditional stacks label alerts that deviate from known patterns as “exceptions,” relegating them to manual queues that rarely get cleared. Radiant’s agentic AI, by contrast, treats those exceptions as the primary subject of inquiry. It employs causal reasoning, hypothesis generation, and iterative evidence gathering to determine whether an outlier represents benign noise or a nascent attack chain. For example, a cross‑domain authentication spike might initially look like a misconfigured service; the AI will check recent privilege‑granting events, examine lateral‑movement indicators, and correlate with any threat‑intel matches before deciding to escalate or dismiss. This approach transforms previously ignored alerts into actionable insights without requiring human intervention at every step.
Eliminating Blind Spots Without Adding Headcount
Because Radiant’s AI conducts the full investigative cycle autonomously, organizations can achieve comprehensive alert coverage without hiring additional analysts or expanding their SOC footprint. The platform reduces mean‑time‑to‑detect (MTTD) and mean‑time‑to‑respond (MTTR) by handling the labor‑intensive correlation and enrichment steps that would otherwise consume analyst hours. Security leaders can therefore reallocate existing talent toward strategic initiatives—such as threat hunting, vulnerability management, or security architecture improvements—while confident that the AI is continuously monitoring and triaging the full spectrum of alerts, including those that previously slipped through the cracks.
Webinar Insights from Shahar Ben‑Hador
In the recorded webinar, Radiant Security CEO Shahar Ben‑Hador elaborates on how alert fatigue undermines even the most mature security teams. He recounts his tenure as CISO at Imperva, where he observed that skilled analysts were often overwhelmed by volumes of low‑value notifications, leaving critical threats undetected. Ben‑Hador argues that the industry’s reliance on playbook‑driven automation—which demands constant tuning and maintenance—fails to adapt to the evolving threat landscape. Likewise, AI solutions that are merely bolted onto legacy stacks inherit the same limitations, offering incremental gains rather than transformative change. His perspective set the stage for founding Radiant, whose mission is to deliver accurate, autonomous triage that eliminates the need for perpetual rule‑writing and alert‑chasing.
Author Bio: Shahar Ben‑Hador’s Journey
Shahar Ben‑Hador began his career as an IT Administrator, progressing through various technical and leadership roles at Imperva before becoming its first Chief Information Security Officer. After nearly a decade shaping Imperva’s security posture, he moved to Exabeam, where he led product strategy and gained deep insight into the strengths and shortcomings of existing SIEM and UEBA solutions. These experiences reinforced his belief that the bottleneck in modern security operations is not talent but the inadequacy of current tools. Together with longtime colleague Barry Shteiman, he founded Radiant Security to create an AI‑native platform that bypasses the need for playbooks, constant tuning, and human‑in‑the‑loop triage, delivering a more resilient security operation.
The Core Problem: Tools, Not People
Both Ben‑Hador and Shteiman contend that the prevailing narrative blaming analyst shortage or skill gaps misses the true root cause: the tools security teams rely on are fundamentally misaligned with the nature of today’s attacks. Playbook‑driven automation excels at handling known, repetitive scenarios but cannot adapt to novel, low‑frequency tactics without continuous human updates. AI that is merely layered atop legacy rule engines inherits the same brittleness, often producing overwhelming numbers of false positives or overlooking subtle, multi‑vector intrusions. Consequently, analysts spend countless hours chasing noise while genuine threats linger undetected. Radiant’s approach flips this model by providing an AI that reasons independently, adapts to new data sources, and scales without the operational overhead that plagues conventional solutions.
Radiant’s AI‑Native Architecture and Call to Action
Radiant Security is built as an AI‑native system: there are no static playbooks to maintain, no tuning cycles to schedule, and no reliance on brittle correlation rules. The platform’s autonomous agents ingest alerts, enrich them with contextual data from disparate domains, apply causal reasoning, and either resolve the incident or surface a concise, evidence‑backed recommendation for human review. This yields accurate, consistent triage at machine speed, dramatically reducing the dwell time of threats that would otherwise remain hidden. For security leaders interested in seeing how Radiant can close their organization’s alert blind spots, the company invites them to book a live demo via the provided link.
Final Note: Partner Content and Social Follow‑Up
This article is a contributed piece from one of our valued partners, offering an exclusive look at Radiant Security’s vision and technology. Readers who found the discussion valuable are encouraged to follow the publication on Twitter and LinkedIn for additional insights, upcoming webinars, and partner‑generated content that explores the cutting edge of AI‑driven security operations. Staying connected ensures access to the latest strategies for overcoming alert fatigue, strengthening threat detection, and building a more proactive defense posture.