AI SOC as a Service — Enterprise Threat Detection for Small Business
ARIA combines AI-driven behavioral analysis with human analyst oversight to deliver a Security Operations Center that runs 24/7, catches threats that signature tools miss, and fits any small business budget.
Traditional security tools work by matching known patterns: signatures, hashes, IP reputation lists. This approach works reasonably well against known threats — malware that has been catalogued, IP addresses already on blocklists, file hashes already seen in the wild. But the threats that cause the biggest losses are the unknown ones: novel ransomware variants, legitimate credentials used maliciously, attacker activity that lives entirely off-the-land using built-in system tools. These threats are invisible to signature-based defenses. Catching them requires behavioral analysis — understanding what normal looks like in your specific environment and detecting deviations from it. This is exactly what ARIA does.
Why AI Detection Catches What Signatures Miss
Signature-based detection is binary: a file or activity either matches a known bad pattern or it does not. An attacker who modifies their tooling slightly, uses legitimate system utilities, or compromises valid credentials will generate no signature matches at all — even as they move through your network, escalate privileges, and exfiltrate data.
ARIA's behavioral detection engine builds a baseline of normal activity for your specific environment: which users typically log in at what hours, from which locations, which systems they access, what commands they typically run. When activity deviates from this baseline — a login from an unusual country, an administrator account accessing systems it has never touched before, a workstation suddenly executing PowerShell commands — ARIA flags it regardless of whether it matches any known signature.
This approach is particularly effective against the attack techniques that are responsible for the largest losses: business email compromise (which uses legitimate email accounts), living-off-the-land attacks (which use legitimate system tools), and insider threats (which use legitimate credentials). None of these techniques trigger signature-based alerts — all of them trigger ARIA's behavioral detection.
- Behavioral baselining per user, device, and application
- Anomaly detection independent of known-bad signatures
- Detection of living-off-the-land techniques
- Legitimate credential misuse detection
- Novel malware family detection via behavioral patterns
- Insider threat detection through access anomalies
Why AI Alone Isn't Enough
AI detection platforms generate alerts. Lots of them. Without human analyst review, the result is alert fatigue — security teams drowning in low-confidence detections, unable to identify which alerts actually matter. This is the failure mode that has made many AI security products unpopular: they detect more things, but they also create more noise, and the signal gets lost.
ARIA solves this with a human-in-the-loop model. Every alert generated by our AI platform is reviewed by a security analyst before it reaches you. Our analysts triage the alert against the full context of your environment, classify it, and determine whether it warrants your attention — and if so, exactly what action to take. You only hear from us when something actually matters.
This model — AI for coverage and speed, humans for judgment and context — is how enterprise security teams have operated for years. ARIA makes it available to businesses of any size.
What Powers ARIA Detection
ARIA ingests telemetry from multiple data sources simultaneously: endpoint agent data (process execution, file system activity, network connections), cloud API logs (Microsoft 365, Azure AD, Google Workspace), network metadata, DNS resolution logs, and email metadata.
This multi-source telemetry is correlated in real time using a graph-based detection engine that identifies relationships between events across different data sources. An attacker who exploits a phishing email (detected in email logs), establishes a reverse shell (detected in endpoint network telemetry), and accesses a file share (detected in SMB logs) triggers a correlated, high-confidence detection — not three separate low-confidence alerts.
The platform continuously updates its behavioral baselines as your environment evolves. New employees, new devices, new applications — the system adapts. When an anomaly truly is anomalous relative to the current state of your environment, the confidence in the detection is high.
- Multi-source telemetry correlation in real time
- Graph-based attack path detection
- Continuously updated behavioral baselines
- MITRE ATT&CK framework alignment
- Threat intelligence enrichment
- Automated alert triage with analyst review
Enterprise Security, Small Business Price
Building an equivalent in-house capability requires a SIEM platform ($50,000–$200,000/year), an EDR solution ($15–$50/endpoint/year), a threat intelligence feed subscription, a 24/7 analyst team (minimum 4–5 FTEs at $80,000–$130,000 each), and a security engineer to maintain it all. The total cost easily exceeds $500,000 per year.
ARIA delivers this same capability — AI detection, multi-source telemetry, human analyst review, 24/7 coverage — as a managed service starting at $799/month. The economics are straightforward: you get a mature security program at a fraction of the cost of building one.
For small businesses in regulated industries, this is not just a financial argument. HIPAA, PCI-DSS, and state data protection laws require technical security controls that most small businesses are not meeting. ARIA provides those controls and the documentation to prove it.
Frequently Asked Questions
Everything you need to know before getting started.
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The Threats Are AI-Powered. Your Defense Should Be Too.
Attackers are using machine learning to evade detection, find vulnerabilities, and scale their operations. ARIA gives small businesses access to the same AI-driven detection capabilities that enterprise security teams rely on — at a fraction of the cost.