AI SOC as a Service — Enterprise Threat Detection for Small Business

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 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 an analyst-in-the-loop model. Every escalation from ARIA is reviewed by a security engineer before it reaches you. Escalations are reviewed in context, classified, and include recommended actions. You only hear from ARIA when something actually matters.

This model — AI for coverage and speed, human judgment for 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 on escalations

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, analyst-reviewed escalations, continuous automated monitoring — 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.