Vega’s $65M Bet: Replacing SIEM with an AI‑Native SAM for a New Era of Threat Detection

When a startup from Israel announces a $65 million round to upend an entrenched category, the ripples are felt far beyond the startup’s headquarters. Vega’s latest financing is not just capital; it is a stake in a vision that argues legacy Security Information and Event Management (SIEM) systems are no longer fit for purpose in an era of pervasive cloud, ephemeral infrastructure, and adversaries who move faster than human-led rulebooks can follow. Vega proposes a replacement: an AI‑native SAM—Security Analytics and Monitoring—designed from the ground up to treat telemetry as living signal, not static log relics.

The problem with SIEM: scale, noise, and brittle logic

SIEM systems dominated cybersecurity for two decades by aggregating logs, correlating events, and surfacing alerts. They became the backbone of security operations centers (SOCs), compliance reporting, and incident investigations. Yet those strengths masked deep weaknesses as environments shifted to cloud-native microservices, containers, and third‑party APIs.

• Data deluge: Modern environments generate petabytes of telemetry. SIEMs were engineered around event rules and indexed logs—not continuous probabilistic models—so storage and query costs balloon and latency grows.
• Alert fatigue: Rule-based correlation yields high false-positive rates. SOCs drown in noise, with analysts chasing noisy signals instead of focusing on anomalous, high‑value incidents.
• Manual tuning: Rules and signatures need constant human maintenance as infrastructure and attacker tactics evolve—an unscalable treadmill.
• Context gaps: Legacy systems struggle to fuse diverse signals—network telemetry, host data, identity context, cloud APIs, application traces—into coherent narratives about attacker behavior.

These architectural mismatches make SIEMs costly, slow, and, ultimately, brittle. Vega’s thesis: treat observability as an AI problem rather than a logging problem.

What is AI‑native SAM?

AI‑native SAM reframes security monitoring around continuous, model-based analysis of signals. Rather than relying primarily on explicit correlation rules, SAM systems use a layered set of techniques—unsupervised and supervised machine learning, representation learning, entity behavior modeling, streaming analytics, and causal inference—to detect deviations and fuse context in real time.

Key characteristics of AI‑native SAM:

• Signal fusion: Ingests and normalizes telemetry from clouds, containers, IAM, endpoints, applications, and threat feeds, creating unified entity profiles that evolve in real time.
• Behavioral modeling: Learns normal across users, devices, and services, surfacing subtle deviations instead of only pre‑coded signatures.
• Contextual reasoning: Correlates anomalies across identity, network, and workload layers to prioritize incidents that indicate true compromise.
• Continuous learning: Models adapt to infrastructure changes and legitimate behavior drift with minimal manual intervention.
• Autonomous triage: Automatically reduces noise by grouping alerts into storylines, assigning confidence scores, and recommending focused investigation steps.

Why $65M matters

Raising $65 million signals both market confidence and runway to build beyond a point product. For a company promising to displace core security infrastructure, capital fuels several urgent needs: scale engineering to handle high‑volume telemetry, investment in model training and governance, integrations across cloud and endpoint ecosystems, and field deployments to prove reliability in production SOCs.

More broadly, bettors placing meaningful capital behind AI‑native security architectures indicate a tectonic shift in how the market expects detection and response to evolve. Customers are tired of stitching logarithms and rules together; they want systems that reduce human toil while increasing fidelity.

Technical tradeoffs and the path forward

No architectural pivot is without tradeoffs. AI systems introduce new complexities around model bias, drift, explainability, and adversarial manipulation. SAM architects must contend with:

• Explainability: Analysts need readable rationales for why a model flagged an incident. Storylines, causal traces, and human‑friendly summaries are necessary to bridge model inference and human judgment.
• Model governance: Training data lineage, versioning, and metrics for false positives and false negatives become first‑class operational artifacts.
• Adversarial resilience: Attackers study detection models. Systems must be hardened against poisoning and evasion attempts, and detection models must be evaluated continuously.
• Privacy and compliance: Telemetry often contains sensitive data. SAMs should support data minimization, on‑prem or hybrid deployments, and privacy‑preserving learning techniques.

Those who succeed will combine robust ML operations, transparent reasoning, and a security‑first approach to systems engineering.

Operations transformed: from noise to narratives

One of the most transformative promises of SAM is not raw detection improvement but operational change. Human analysts spend much of their time validating alerts, chasing forensic breadcrumbs, and hunting for context across disparate tools. SAM reframes alerts as narratives—constructing incident timelines automatically, surfacing pivot points, and suggesting containment actions. The result: SOCs can triage faster and allocate human expertise to nuanced investigation and strategic problems instead of sifting through noise.

That shift has implications for workforce strategy. It reduces repetitive tactical work and elevates roles focused on threat hunting, adversary emulation, and incident strategy. Training and tooling will need to evolve in tandem: playbooks and interfaces must mirror model outputs and accommodate analyst feedback loops that improve models.

Market dynamics and competition

Legacy SIEM vendors will not disappear overnight. Many have deep enterprise footprints and regulatory use cases. But startups that can demonstrate materially better performance—fewer false positives, faster mean time to detection, lower total cost of ownership—will compel migration. Expect a few dynamics:

• Integration pressure: Vendors will bolt AI capabilities onto existing SIEMs, accelerating a hybrid transition period where rule‑based and model‑based systems coexist.
• Platform consolidation: As SAMs prove they can manage telemetry at scale, customers may prefer platforms that unify detection, response, and compliance flows.
• M&A activity: Larger security and cloud providers may acquire focused SAM startups to stitch AI‑native detection into bigger stacks.

Adversaries will adapt; the race continues

Every leap in defensive technology triggers offensive adaptation. More sophisticated detection will push attackers to simulate legitimate behavior, operate at low-and-slow tempos, and exploit supply chains or inference blind spots. SAM systems must therefore be designed for continuous arms‑racing: threat intelligence integration, red‑team feedback, and rapid retraining cycles will be core components of resilience.

Why the Israeli context matters

Israel’s cybersecurity ecosystem has long been a crucible for innovation, spawning technologies that reshape enterprise security worldwide. Vega’s funding is part of that narrative—entrepreneurial teams, deep operational threat experience, and dense networks of talent. The country’s dense security R&D culture accelerates iteration on hard problems like streaming telemetry processing, low‑latency inference, and distributed model serving—capabilities that underpin successful AI‑native SAMs.

The broader AI question: trust, transparency, and governance

AI brings power and peril. Building trust into security systems requires not only model performance but also governance standards that make model decisions auditable and defensible. For organizations operating in regulated industries, compliance demands will shape SAM adoption: explainability for regulators, data residency controls, and certified assurance practices will be prerequisites.

Think of SAM not as magic but as a new class of infrastructure that demands the same operational rigor we apply to storage, networking, and compute. The conversation must move from whether AI can help to how AI can be engineered, governed, and operationalized safely.

What to watch next

1. Deployments at scale: Real‑world case studies measuring detection rate, false positives, and mean time to resolution will determine market momentum.
2. Interoperability: How SAM platforms integrate with existing SOAR playbooks, ticketing systems, and cloud providers will ease migration.
3. Open frameworks: Standards for telemetry schemas, model evaluation, and incident storylines will accelerate adoption if communities coalesce.
4. Adversary response: New offensive tactics designed to evade AI models will reveal the robustness of SAM designs and the speed of countermeasures.

Conclusion: a decisive moment for security architecture

Vega’s $65 million raise is a bold statement: the future of detection is not an upgraded rule engine but an AI‑native architecture that treats telemetry as continuous, high‑dimensional signal. This is not merely about better alerts. It’s about reimagining how organizations understand, respond to, and ultimately outpace digital threats.

For those building and defending modern systems, this is an invitation to rethink foundational tooling. The coming months will test whether AI‑native SAMs can deliver on promises of clarity, speed, and scale—and whether the security community can match technological promise with governance, interpretability, and adversarial resilience.