Bypassing the SIEM Tax: How Mave's $34M Agentic Security Engine Queries Tools at the Source

The Log Ingestion Trap: Why Feeding Central SIEMs Breaks Under AI-Speed Attacks

For two decades, corporate cybersecurity operations operated on a single, expensive assumption: to secure an enterprise, you must first copy every piece of operational telemetry into one giant central database. Security Information and Event Management (SIEM) platforms convinced chief information security officers that unless server logs, network flows, cloud audit trails, and endpoint metrics lived under one roof, detecting sophisticated intruders was impossible.

That model has hit a hard physical and financial wall.

Modern enterprise networks generate dozens of terabytes of telemetry every single day. Ingesting, indexing, and retaining that ocean of raw data costs Fortune 500 corporations between $1.5 million and $8 million annually in software licensing and cloud storage fees alone. Because logging bills grow faster than corporate IT budgets, security leaders are forced to make dangerous compromises. SOC directors selectively prune log streams, discarding identity access logs, ephemeral container records, and granular API traces simply to avoid budget overruns. As a result, critical context never enters the central repository.

When an alert triggers, an analyst quickly discovers that the SIEM only shows half the picture. To understand what actually happened, the human operator must log into an endpoint console, open a cloud identity portal, query a firewall interface, and check an email gateway. Attackers do not wait for analysts to copy and paste IP addresses between browser tabs. Modern automated threat campaigns move laterally across corporate networks in minutes, while the humans tasked with defending those networks burn 30 to 60 minutes per incident simply gathering initial evidence.

Tel Aviv-based startup Mave Security Ltd. emerged from stealth with $34 million in fresh financing to dismantle this centralized ingestion architecture. Founded in 2025 by Israel Defense Forces Unit 8200 veterans Raziel Oren and Guy Eshel, Mave takes the opposite operational path. Instead of moving petabytes of enterprise telemetry into a third-party data warehouse, Mave’s software connects directly to an enterprise’s existing security consoles through native application programming interfaces.

When a suspicious event surfaces, Mave deploys autonomous software agents that query each native tool in its own language, extract the necessary configuration and identity details on the fly, and synthesize the findings into a single coherent incident report.

The company’s commercial trajectory underscores how urgently corporate security teams want relief from the data-ingestion tax. Mave expanded its annual recurring revenue by more than 400% over the past twelve months, securing multiple seven-figure contracts across Fortune 50 accounts and large managed security service providers (MSSPs). The $34 million financing package combines a $28 million Series A led by Red Dot Capital Partners with participation from Lumir Growth Partners, and an earlier $6 million seed round co-led by Dell Technologies Capital and Emerge Ventures.

Rather than selling another dashboard that demands its own proprietary agent on every laptop, Mave sits above existing enterprise investments, transforming disconnected point products into an automated, coordinated defense network.

The $1.8M Data Ingestion Toll: Benchmarking In-Place Agentic Triage Against Legacy Pipelines

To understand why autonomous, federated querying is attracting top-tier venture backing and enterprise buyers, one must examine the operational math of modern security operations centers.

Consider a typical mid-to-large enterprise with 10,000 employees. That organization generates roughly 1.2 terabytes of raw security event data daily across firewalls, cloud infrastructure, endpoints, and SaaS applications. Under traditional central-ingestion architectures, every gigabyte routed through a commercial SIEM carries ingestion fees, hot storage compute costs, and indexing overhead.

The following matrix contrasts traditional SIEM pipelines, secondary data lake architectures, and Mave’s federated agentic model across core operational metrics.

The core failure of the legacy approach lies in what security engineers call the “fidelity gap.” When an endpoint detection tool records a process injection, it logs hundreds of rich attributes: memory offsets, parent-child process lineages, code-signing certificates, and thread injection targets. Because streaming all that forensic data into a SIEM would bankrupt the company, standard data pipelines strip out 90% of those fields, forwarding only basic timestamps, hostnames, and binary names.

When an analyst opens the alert in their SIEM, the crucial clues are missing. The analyst must immediately leave the SIEM, log into the endpoint protection console, locate the raw incident, and pull the complete forensic bundle manually.

Mave’s agentic framework inverts this workflow. Instead of asking how much data the organization can afford to forward to a central repository, Mave asks what questions an investigator needs to answer. The system’s AI agents generate precise, scoped queries directly against the native endpoint API, the cloud identity provider, and the network gateway simultaneously. The agents pull back only the relevant answers, correlate them, and present an actionable dossier before a human analyst could even complete multi-factor authentication into their second console.

Three Operational Pressure Points: How Agentic Consolidation Rewrites SOC Economics

Deploying an autonomous agentic layer directly over an enterprise security stack changes three foundational pillars of day-to-day operations: capital expenditure on data pipelines, security analyst throughput, and the operational blast radius of active intrusions.

Slashing Telemetry Ingestion OPEX by Eliminating the Central Data Tax

Enterprise security budgets are finite. When a company spends 40% of its operational security budget simply paying a vendor to store and index raw firewall syslogs, that capital cannot be spent hiring experienced threat hunters, upgrading infrastructure, or conducting penetration testing.

In a standard enterprise generating 1 TB of telemetry per day, routing data through tools like Datadog or traditional SIEM platforms requires careful data tagging, retention tiering, and aggressive drop-rules. Engineering teams spend hundreds of hours every quarter maintaining data ingestion pipelines, updating parsing rules when log formats change, and arguing over which server groups can afford to turn on verbose debugging logs.

By establishing an agentic query mesh, Mave decouples threat detection coverage from log storage volume. The enterprise keeps its raw operational data inside the source platforms that generated it. Endpoint logs remain in the endpoint detection cache; cloud audit trails stay in object storage; identity events remain inside identity provider logs. Mave queries these systems via native APIs only when an anomaly, alert, or threat hunt requires inspection.

Based on conservative enterprise spending profiles, eliminating SIEM ingestion surcharges saves a 5,000-seat organization between $350,000 and $800,000 annually in log parsing compute and tier-1 hot storage charges.

Compressing Investigation Lead Time from 45 Minutes to Under 180 Seconds

The primary driver of SOC analyst burnout is repetitive, low-level data collection. Tier-1 and tier-2 analysts spend upwards of 70% of their working hours performing the same mechanical routine: copy an IP address, run it through an external threat intelligence tool, open the active directory console to check user department and permissions, search the firewall log to see if traffic crossed outbound, and draft a summary note in a ticketing queue.

Mave automates the entirety of this investigative legwork. Because its platform models the problem-solving logic of an experienced Unit 8200 cyber investigator, the AI agents know which questions to ask based on the initial alert type:

• Phishing Alert Triggered: The agent autonomously checks the email gateway for recipient headers, queries the browser isolation tool to see if the user clicked the embedded link, checks the endpoint agent to determine if an unverified payload was written to disk, and interrogates the identity provider for impossible travel anomalies on that user account.
• Credential Stuffing Alert Triggered: The agent checks authentication logs across corporate SaaS apps, matches source IP subnets against known proxy exit nodes, and determines whether any compromised session successfully established an active administrative token.

By executing these cross-console checks concurrently, Mave completes an investigation cycle in two to three minutes. False positives are dropped automatically without human triage, while genuine attacks are escalated with a complete chronology of events, verified compromise artifacts, and pre-calculated blast radiuses already mapped out.

Hardening Threat Containment Without Granting Runaway AI Execution Permissions

The single biggest fear enterprise CISOs express regarding artificial intelligence in security operations is the risk of an unguided model executing destructive containment actions by mistake. If an autonomous system hallucinates and erroneously shuts down a primary database cluster or locks out the Chief Executive Officer’s credentials during a quarterly earnings release, the security software causes more operational downtime than the attacker would have.

Mave addresses this operational risk by separating autonomous evidence gathering from policy-governed containment execution.

While the system’s AI agents have full autonomy to read, correlate, and cross-reference forensic telemetry across every connected tool, remediation actions operate strictly within deterministic, user-defined guardrails. Organizations establish granular operational boundaries: an agent may be permitted to isolate a staging laptop or revoke a temporary session token automatically, while actions affecting mission-critical production servers, domain controllers, or executive identities require explicit human approval via a single-click authorization prompt.

During live corporate attacks cited by Mave, this governed execution loop isolated compromised hosts and revoked hijacked credentials before initial intrusions could escalate into network-wide ransomware deployments, proving that high-speed AI containment can coexist with enterprise safety controls.

Federated Queries vs. Data Lakes: How Early Adopters and MSSPs Manage Enterprise Defense

The cybersecurity industry has witnessed multiple attempts to fix the SIEM problem. To contextualize Mave’s approach, security architects must understand how federated agentic systems differ from the other major alternative currently gaining traction: the centralized cloud data lake.

Over the past four years, many enterprise architectures migrated away from proprietary SIEM storage formats toward open cloud lakehouses built on modern data platforms. The pitch was simple: store all security data in low-cost cloud storage buckets, run analytics via standard SQL engines, and sidestep the predatory pricing of legacy SIEM vendors.

While security data lakes succeed at reducing storage costs, they do not solve the investigative latency problem. Moving raw logs into a data lake still requires complex data engineering pipelines, constant schema normalization, and long query execution windows. When a live ransomware operator is moving through a corporate subnet, an incident responder cannot wait fifteen minutes for a massive distributed SQL query to scan 40 terabytes of unindexed parquet files.

Managed Security Service Providers (MSSPs) represent the clearest testing ground for these competing approaches. An MSSP managing security operations for 300 mid-market companies cannot afford to deploy, maintain, and pay ingestion costs for 300 separate central SIEM instances. Nor can their tier-1 analysts effectively pivot between 300 distinct customer firewall dashboards every hour.

By deploying Mave, global MSSPs connect directly to their clients’ existing endpoint and cloud configurations without building custom data extraction bridges. The software acts as an automated force multiplier: tier-1 triage is handled entirely by the agentic layer, allowing a single senior human analyst to oversee security posture across dozens of distinct client environments.

For enterprise environments that already run sophisticated cloud-native telemetry pipelines via platforms like Datadog for infrastructure monitoring, Mave acts as an intelligent connective tissue. Rather than replacing the telemetry systems already deployed across the fleet, it bridges the operational gap between infrastructure visibility and rapid threat remediation.

Deployment Verdict: Fit Matrix and Operational Boundary Conditions

Adopting an agentic security architecture represents a structural shift from passive log storage to active, distributed automation. While Mave’s $34 million war chest and rapid growth signal widespread market appetite, the platform is not an identical fit for every operating model. Security leaders must assess their existing infrastructure maturity, regulatory constraints, and internal engineering resources before moving away from centralized log paradigms.

When to Deploy Immediately (3 Clear Fit Factors)

1. Your Organization Operates More Than 8 Disconnected Security Consoles: If your security operations team spends significant portions of their shifts jumping between distinct endpoint detection, identity management, cloud posture, and email security dashboards, Mave delivers immediate ROI. The platform eliminates the “swivel-chair tax,” allowing analysts to review single-page consolidated incident accounts rather than manually piecing together evidence.
2. SIEM Telemetry Ingestion Overages Exceed $300,000 Annually: If your infrastructure and security teams spend recurring engineering cycles negotiating log retention limits, pruning firewall telemetry, or disabling verbose cloud audit trails simply to stay within budget, adopting an in-place querying engine breaks the cycle. You stop paying to move data you rarely inspect.
3. You Operate an MSSP or Multi-Tenant Security Services Practice: For managed providers protecting hundreds of mid-market corporate clients, deploying and maintaining separate SIEM instances for each customer destroys operating margins. Mave’s agentic integration allows a lean centralized team of analysts to triage and contain incidents across highly diverse customer stacks without standardizing the underlying tooling.

When to Hold Back and Wait (3 Serious Non-Fit Risks)

1. Your Compliance Regime Demands Centralized Raw Log Custody: If your organization operates under strict defense (CMMC), government, or specific international financial mandates that legally require raw, cryptographically signed log files to be held in a central, immutable repository for 3 to 7 years, Mave cannot replace your compliance storage layer. You will still need an object-storage lakehouse or cold archive for regulatory auditing, even if you use agentic systems for active alert triage.
2. Your Core Infrastructure Relies on Bespoke, Legacy On-Premises Systems: Mave’s agentic model requires modern, well-documented REST APIs to query native tools on the fly. If your enterprise defense relies on custom, home-grown network monitors, legacy mainframe audit logs, or proprietary appliances lacking robust programmatic interfaces, autonomous agents will be unable to extract context at the source.
3. Your Organization Enforces an Absolute Ban on Third-Party Generative AI Intermediaries: While Mave emphasizes deterministic execution guardrails and role-based access limits, its reasoning core depends on frontier artificial intelligence models to reconcile native tool outputs and construct natural language threat chronologies. Highly compartmentalized defense or intelligence enclaves that operate in completely air-gapped environments cannot currently accommodate cloud-hosted agentic reasoning engines.

Mave’s rapid emergence marks a decisive turning point in enterprise cybersecurity. The era of spending millions of dollars simply to assemble mountains of unparsed logs inside a central database is closing. By allowing autonomous agents to go directly to where the data lives, security operations teams are finally matching the speed, flexibility, and coordination of the automated attackers they defend against.