Security

Lock down your
AI attack surface

Model allowlisting, anomaly detection rules, insider threat signals, and a tamper-evident audit chain. FORG gives security teams the visibility and controls they need before AI becomes your next unmonitored data exfiltration vector.

Lock down your AI attack surface Talk to our security team

68%

of AI security incidents

involve unauthorized model use — not prompt injection

3.2×

Faster threat detection

with FORG anomaly rules vs. manual log review

100%

Sessions logged

with tamper-evident hash chain from day one

The threat

Your employees are using AI models you've never approved — and you'd have no way to know.

Most organizations have a "use Anthropic, not OpenAI" policy that exists in a wiki nobody reads. There's no enforcement layer. Users swap models because one feels faster, a GitHub Action pulls in a different provider quietly, or a contractor brings their own key.

The real risk is behavior, not just the model: an engineer who pastes customer PII into a context window at 2 AM, a contractor running bulk inference on your codebase over a weekend, a budget spike that signals automated exfiltration.

FORG sits in the API path. Every call hits FORG first. Model check → rule evaluation → signal emission → optional block. Zero-trust by design: if it's not explicitly allowed, it doesn't go through.

Model allowlist enforced at the proxy layer — not on the honor system
Anomaly rules fire on behavior, not just model selection
Off-hours and bulk patterns flagged within seconds
Zero prompt content stored — metadata-only reduces your data surface

ANOMALY ALERTS — LAST 24H3 ALERTS

UserEvent typeSev.Rule triggeredAction

m.lee@corp.iounusual_model_accessHIGHModel not in org allowlist: gpt-4-turboBLOCKED

d.ross@corp.iobudget_spike_3xMED3× daily avg in 2h — $42 / $14 baselineNOTIFIED

r.chan@corp.iooff_hours_heavy_useMED68 requests 02:14–04:02 UTC, SatNOTIFIED

Rules evaluated: 1,284SIEM export: activeAudit chain: verified

What your AI security posture looks like with and without FORG

The difference between a discoverable incident and one that goes undetected for months.

Without FORG

Model policy is a wiki page — no technical enforcement
Unauthorized model access only discovered in post-incident review
No behavioral baseline — impossible to define "anomalous" usage
Audit log gaps — provider logs only capture successful API calls
Insider threat detection relies on employee self-reporting
SIEM has no AI-layer events to correlate against other alerts

With FORG

Allowlist enforced at the proxy — unapproved models hard-blocked
Anomaly rules alert and optionally block within seconds
Behavioral baseline auto-built per user; anomalies surface automatically
100% of sessions in tamper-evident hash chain from first request
Insider threat patterns (bulk off-hours, spike, model switching) auto-detected
Splunk, Datadog, and generic SIEM export via webhook or syslog

Purpose-built controls for the AI threat surface

Not a CASB bolt-on. Not a DLP repurpose. Built from first principles for AI API traffic.

Model Allowlist Enforcement

Define the org-approved model list. Every request checked before reaching the provider. Non-allowlisted models return a 403 with a policy violation event in the audit log.

Anomaly Detection Rules

Configurable rules: off-hours usage, budget spikes (N×), request volume bursts, model switching, unusual context window sizes. Alert, throttle, or hard-block per rule.

Immutable Audit Chain

Every session hashed and chained. SHA-256 hash of each event includes prior-event hash — tampering any record breaks the chain. Cryptographically verifiable in discovery.

Insider Threat Detection

Bulk off-hours requests, large context windows with file reads, rapid model switching, sudden spend spikes — FORG surfaces the behavioral patterns that precede exfiltration.

Zero-trust Session Logging

Every AI session logged regardless of outcome. Zero prompt content stored — metadata only. No data surface liability. Logs satisfy SOC 2 CC7 and ISO 27001 A.12.4.

SIEM Integration

Emit events to Splunk, Datadog, Elastic, or any webhook endpoint. Each anomaly event includes user, session, rule triggered, model, and classification. Drop-in SIEM correlation.

AI threat surface