“Promising” means something specific to IT professionals: a company is promising when its product can survive enterprise reality—identity sprawl, hybrid networks, compliance pressure, constrained budgets, legacy dependencies, and the need for measurable outcomes. In 2026, Israel’s startup ecosystem continues to produce companies that build for those constraints: cloud security that assumes multi-cloud by default, data protection that treats AI as a new exfiltration path, FinOps that pushes cost ownership back to engineering, and deep tech that is finally showing up as deployable infrastructure.
The goal here is practical: give IT teams a shortlist of Israeli startups worth tracking, piloting, or integrating—without turning your environment into a lab. The companies below span security, cloud operations, AI infrastructure, deep tech, and operational intelligence. They are presented with an IT lens: how they tend to fit into real stacks, what to validate during a pilot, and why they matter in 2026.
Companies to track in 2026
This is a quick index (no ranking). Details follow for each company.
- Wiz
- Cyera
- Noma Security
- Cato Networks
- Axonius
- Orca Security
- Snyk
- Armis
- Claroty
- Silverfort
- Aporia
- PointFive
- Coralogix
- Buildots
- Exodigo
- Quantum Machines
- Hailo
- AI21 Labs
- Bria.ai
- Lightricks
- Gong
- FundGuard
- Rapyd
- Fireblocks
Wiz
Wiz is a strong reference point for where cloud security has landed in 2026: fast coverage across multiple cloud providers, a bias toward “find the path to impact,” and workflows built around remediation ownership. For IT professionals, Wiz typically matters when you need a unified view across AWS/Azure/GCP (and increasingly SaaS and container layers) without forcing every team into a new toolchain.
What to validate in a pilot: identity integration (SSO/SCIM), least-privilege cloud access, signal quality (does it reduce noise or create it), and whether remediation maps cleanly to your ticketing/CI pipelines. The win condition is not “more findings,” but fewer high-risk exposure paths that are measurable over time.
Cyera
Cyera represents a major trend IT teams feel every day: data is everywhere, and the hardest part of securing it is visibility with context. Data Security Posture Management (DSPM) is increasingly treated as foundational—especially as AI tools multiply the ways sensitive data can be copied, transformed, and shared.
What to validate in a pilot: how quickly it discovers and classifies data across your actual data estate, how it handles structured and unstructured stores, and whether it can drive concrete actions (policy enforcement, access tightening, encryption/retention alignment) rather than generating yet another “data inventory report” no one owns.
Noma Security
AI is not “one application” anymore—it is a layer that touches identity, data, endpoints, and workflows. Noma Security sits in the emerging space of securing AI applications and agent-style automation. For IT professionals, the practical value is governance: what data an AI workflow can access, what it can export, which tools it can invoke, and how all of that gets audited.
What to validate in a pilot: coverage across your AI stack (model providers, vector databases, data sources, productivity suites), enforcement options (block, redact, approve, monitor), and audit fidelity (can you reconstruct an incident and prove controls worked). The best outcome is fewer “unknown AI paths” in your environment.
Cato Networks
SASE is less of a buzzword in 2026 and more of a design pattern: consolidate networking and security control planes so remote work, branch connectivity, and cloud access remain governable. Cato Networks is often evaluated when teams want fewer appliances, simpler policy enforcement, and a consistent security posture across users and sites.
What to validate: latency and user experience, integration with your identity stack, segmentation approach, log export into your SIEM/data lake, and the operational model (how changes are approved, tested, and rolled back). The SASE win is operational simplicity without losing control.
Axonius
You cannot secure what you cannot reliably inventory—especially with modern device fleets, SaaS sprawl, and shadow cloud assets. Axonius focuses on cyber asset management: connecting many data sources (EDR, MDM, IAM, cloud, vuln scanners) to produce a high-confidence picture of “what exists” and “what is missing.”
What to validate: connector depth for your environment, data normalization quality, and whether “asset truth” can drive automation (quarantine devices, enforce MDM enrollment, remove stale accounts, close gaps) in a controlled way.
Orca Security
Orca Security is commonly associated with agentless cloud security approaches that aim to cover vulnerabilities, misconfigurations, and risky identities without requiring endpoint agents everywhere. In 2026, that’s especially relevant for ephemeral workloads, serverless components, and environments where agent rollout is a political or technical bottleneck.
What to validate: coverage across accounts/subscriptions/projects, the granularity of findings, the clarity of attack paths, and how remediation integrates with engineering ownership. The right platform reduces friction, not just increases visibility.
Snyk
Developer security is now inseparable from delivery speed. Snyk is widely used for scanning dependencies and code, and the operational challenge in 2026 is not “can we detect issues,” but “can we fix issues without stopping delivery.” For IT, it matters because secure-by-default pipelines reduce downstream incident cost and reduce firefighting pressure on platform teams.
What to validate: integration with your repositories and CI, policy controls (what blocks builds vs what alerts), and remediation workflows that developers will actually accept. The goal is fewer recurring high-risk packages and a declining backlog you can measure.
Armis
Asset intelligence for unmanaged devices is becoming a core IT and security requirement: medical devices, building systems, IoT, industrial sensors, and “mystery” devices that show up on networks. Armis is often discussed in that context, where the outcome is better segmentation, faster incident response, and fewer blind spots in critical environments.
What to validate: passive discovery accuracy, device identification depth, integration with NAC/segmentation tools, and how quickly you can operationalize policies without breaking business workflows.
Claroty
OT/ICS security continues to converge with IT security, but the operating constraints remain different: safety, uptime, and vendor-supported configurations often limit classic security controls. Claroty is a known player in this space, and it remains relevant as more industries treat operational resilience as a board-level risk.
What to validate: deployment model (passive vs active), support for your specific protocols and device families, segmentation strategy, and how OT visibility feeds your enterprise incident response process without overwhelming OT teams.
Silverfort
Many organizations still rely on legacy authentication surfaces: service accounts, older protocols, and applications that cannot be modernized quickly. Silverfort is often evaluated for strengthening identity security and MFA coverage across those harder-to-control areas, which is especially useful during modernization programs and hybrid identity transitions.
What to validate: compatibility with your AD/AAD identity model, enforcement behavior during edge cases, and logging that can feed your detection and compliance workflows. The value is reducing “identity exceptions” that attackers love.
Aporia
As GenAI systems enter production, monitoring and guardrails are becoming as important as model selection. Aporia operates in the space of AI observability and governance—helping teams detect drift, unsafe outputs, data leakage risk, and reliability issues in model-driven services.
What to validate: integration with your inference stack and data pipelines, governance controls that match your risk profile, and whether monitoring can be tied to incident response playbooks instead of being “yet another dashboard.”
PointFive
Cloud cost optimization has matured into engineering accountability. PointFive is aligned with that shift: treat cloud waste as a posture problem and drive continuous action, not one-off savings projects. For IT leaders, this matters because “cost work” often reveals reliability and architecture gaps.
What to validate: attribution accuracy (teams, services, environments), safety mechanisms for changes, Kubernetes and commitment strategy support, and exportability of recommendations so you can enforce standards and track outcomes over time.
Coralogix
Observability in 2026 is about keeping signal while controlling cost. Coralogix is often discussed as a log analytics and observability platform where the IT challenge is both technical and financial: storing everything is expensive, but dropping the wrong signals breaks incident response.
What to validate: ingestion and parsing flexibility, alerting fidelity, SIEM/ITSM integrations, retention controls, and whether teams can self-serve safely without causing runaway spend or data chaos.
Buildots
Buildots is part of a larger shift: software is moving deeper into physical operations. For construction-heavy organizations, construction intelligence can become a real enterprise system—touching identity, device management, data retention, contractor access, and governance.
What to validate: data governance for site imagery and telemetry, role-based access for multi-organization collaboration, integration into project tooling, and operational support when connectivity is inconsistent. The IT benefit is turning “project status” into measurable, auditable data.
Exodigo
Exodigo focuses on underground mapping and subsurface intelligence—an example of “deployable sensing + analytics” becoming a software platform. For IT teams, these systems create new data pipelines and governance questions: raw sensor outputs, derived models, long-lived infrastructure records, and decisions tied to high-cost projects.
What to validate: secure ingestion and storage, access control for multi-stakeholder projects, retention policies, and how outputs integrate with GIS/CAD ecosystems and project management. The value is reducing uncertainty and rework in infrastructure work.
Quantum Machines
Quantum Machines reflects deep tech becoming more operational: the control and orchestration layer between quantum hardware and classical compute. For most enterprise IT orgs, the near-term relevance is not replacing existing compute, but preparing for hybrid workflows, secure access to specialized compute environments, and reproducibility requirements that look a lot like modern MLOps.
What to validate (where applicable): secure lab and research access models, auditability of experiments, integration with existing identity and data tooling, and operational support for high-sensitivity environments.
Hailo
Edge AI is accelerating as organizations push inference closer to devices for latency, privacy, and resilience reasons. Hailo is in the category of edge AI processors and acceleration. For IT professionals, the practical impact is architecture: where inference runs, how models are updated, and how you manage fleets of devices that now execute AI locally.
What to validate: device lifecycle management, secure update pathways, telemetry, performance per watt, and whether the software toolchain fits your deployment reality. Edge AI is powerful when it does not become an unpatchable fleet.
AI21 Labs
AI21 Labs is associated with foundation-model development and enterprise-oriented language tooling. In 2026, the IT focus is less about model mystique and more about governance: data handling, isolation, audit trails, and the ability to integrate model services into applications without sacrificing compliance.
What to validate: deployment options, data usage guarantees, identity and access control, observability of prompts and tool calls, and how the product supports policy enforcement (redaction, allow/deny lists, role-based controls).
Bria.ai
Generative media is moving into enterprise production workflows, which makes governance and rights management central. Bria.ai fits the “controlled generative” category where organizations care about brand safety, licensing, and traceability as much as creative output.
What to validate: permission models, provenance/traceability, policy controls, and how content generation integrates into asset management pipelines. The IT win is enabling creativity without creating legal and security exposure.
Lightricks
Lightricks represents the consumer-to-enterprise drift of creative tooling: as teams generate more media assets rapidly, the tooling becomes a governance and collaboration issue, not just a design issue. Security, data residency, access boundaries, and workflow integration become IT responsibilities.
What to validate: account management, enterprise controls, content handling policies, integration with collaboration and storage platforms, and how AI features are governed and audited.
Gong
Revenue intelligence platforms like Gong sit at the intersection of productivity, analytics, and compliance—especially because they often ingest voice, transcripts, CRM data, and sensitive deal information. For IT, the work is ensuring secure integration and responsible retention as usage scales.
What to validate: data retention and deletion, regional controls, encryption posture, identity integration, and the audit trail around who can access recordings, transcripts, and derived insights.
FundGuard
FundGuard is a strong example of “automated trust” in fintech operations. For IT professionals, fintech automation is integration-heavy and audit-heavy: systems must be explainable, resilient, and safe under regulatory scrutiny. Tools that embed controls reduce the gap between policy and production behavior.
What to validate: identity and access controls, auditability of decisions, integration with core systems, incident response processes, and whether the platform supports governance without slowing operations to a crawl.
Rapyd
Payments infrastructure is an enterprise integration challenge disguised as a product. Rapyd sits in the global payments and fintech infrastructure space, where IT teams care about reliability, fraud controls, compliance alignment, and the ability to observe and troubleshoot transaction flows end to end.
What to validate: API maturity, rate limits and resiliency patterns, logging and reconciliation support, sandbox-to-production parity, and controls for fraud signals and dispute workflows.
Fireblocks
Digital asset infrastructure introduces fast-moving risk: key management, transaction approvals, policy enforcement, and auditability. Fireblocks is often referenced in the custody and transfer security space, where the IT problem looks like a blend of HSM governance, identity controls, and high-speed fraud prevention.
What to validate: policy models for approvals, integration with identity providers and SOC workflows, logging fidelity, and recovery procedures. The value is controlling irreversible actions with enterprise-grade governance.
How to evaluate startups safely in enterprise environments
The most successful pilots in 2026 share the same shape: narrow scope, explicit success metrics, strict observability, and rollback-by-design. Start with a bounded environment (one cloud account, one business unit, one data domain, one workflow). Require SSO/SCIM early. Export logs into your central system. Confirm least privilege and document exactly what access is granted.
Define the outcome in operational terms. Security pilots should reduce exposure paths, reduce mean time to remediate, or measurably improve coverage for critical assets. FinOps pilots should produce engineering-owned changes that keep savings durable. AI governance pilots should create auditable guardrails that prevent data leakage and reduce unknown AI behavior in production.
“Promising” becomes “useful” when the tool fits your operating model: change control, policy enforcement, audit readiness, and incident response. The companies above are worth tracking because they tend to align with those realities—and because their categories match the problems IT teams are actually trying to solve in 2026.
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