AI SECURITY
The attackers are already using AI. Your defenses should too.
Intelligent threat detection that learns your systems, predicts vulnerabilities before they're exploited, and responds to incidents in real-time, faster than any human team could.
Why it matters
The case for doing this now.
Traditional security is built for known signatures and human response times. The threat surface is now larger and faster than either: AI-generated phishing, automated reconnaissance, supply-chain compromises, and prompt-injection attacks against the LLM features you just shipped.
We build defensive AI into your stack - continuous monitoring that learns what 'normal' looks like for your systems, flags drift early, and autoresponds to the patterns that don't need a human in the loop.
Behavior drifts from normal
Continuous monitoring- BaselineNormal learned
- DriftFlagged early
- SeverityThreshold checked
- Trusted patternContained automatically
- Everything elseEscalated with context
Defensive AI built into your stack
What’s included
How we ship this.
Threat-model and red-team review
We map your attack surface, including the new AI-specific paths: model APIs, prompt injection, data exfiltration via tool use, and shadow agents.
Anomaly-detection layer
Behavioral baselines per user, per service, per API key - with continuous monitoring and tunable severity thresholds.
Real-time response playbooks
Automated containment for the patterns you trust to a machine, escalation with full context for the ones you don't.
AI-feature hardening
Guardrails, output validation, and abuse-resistant prompts for the LLM features in your own product.
Data points
The numbers behind the case.
$4.45M
average cost of a data breach in 2023
$1.76M
average breach-cost savings for orgs using AI/automation in security
108 days
shorter time to identify and contain breaches with AI/automation
~80%
of breaches involve a phishing or social-engineering vector
Typical rangeIndustry estimate (Verizon DBIR range)
Top 10
OWASP risk: prompt injection is now the #1 risk for LLM apps
40%
of enterprise apps will host AI agents by end of 2026 - each a new attack surface
Where this shows up
What this looks like in practice.
A mid-market SaaS team launching its first LLM-powered feature
We added prompt-injection guardrails, output validation, and rate-limited tool use before launch. Two real attempted exfiltration attacks in the first month were caught and silently dropped.
Representative engagement
A logistics operator with hundreds of API integrations
Behavioral baselines on every API key cut false-positive alert volume by ~70% and surfaced one credential that had been quietly compromised for weeks.
Representative engagement
Guides to help you decide
- AI for Therapy Practices
AI for therapy practices: answer intake calls, book consultations, match clients to clinicians, run waitlists and superbills, with crisis escalation built in.
- AI for MSPs
AI for managed service providers: when AI ticket management pays off, how to triage and document tickets, answer after-hours calls, and scope agent access.
Next step
Get an AI-readiness security review
We'll walk your stack, find the new AI-specific risks alongside the classic ones, and hand you a prioritized fix list - not a 200-page report.