Security for AI Startups
You're seed funding resembles or exceeds most SaaS company A rounds. You're building something transformative. You're planning to be in production in under six months and in 6, 7 or 8 figure bookings within a year. Enterprise customers want what you're building - but they need to trust you with their data first - you have no choice but to build real security into your product and operations from the ground up. We help AI startups build the security programs and guardrails that unlock enterprise deals without slowing innovation.
The Questions Your Customers Are Asking
AI Startups Face Different Security Questions
Your enterprise prospects aren't just asking for SOC 2. They're asking questions your traditional SaaS competitors never faced:
Data & Model Security:
- How is our data used in model training? Can we opt out?
- Where does inference happen - on your infrastructure or ours?
- How do you prevent our proprietary data from leaking into other customers' outputs?
- What happens to conversation logs and user prompts?
AI-Specific Risks:
- How do you prevent prompt injection attacks?
- What safeguards prevent your model from generating harmful content?
- How do you handle adversarial inputs designed to extract training data?
- What's your approach to model output filtering and safety?
AI Compliance & Governance:
- How does your AI comply with emerging regulations (EU AI Act, ISO-42001, state laws)?
- What documentation exists for model provenance and training data lineage?
- How do you handle bias detection and fairness auditing?
- What's your approach to explainability and decision transparency?
Vendor & Supply Chain:
- Which foundation model providers do you use (OpenAI, Anthropic, open-source)?
- How do you evaluate the security of your AI supply chain?
- What happens if your model provider changes terms or access?
These questions require security leadership that understands both traditional enterprise security and the unique risks of AI systems.
How We Work with AI Startups
Security That Moves at AI Speed
We've worked with AI startups from seed stage through Series E. We understand the pressure - investors pushing for growth, customers demanding enterprise features, and a competitive landscape where being first matters.
Our Approach:
Phase 1: AI Security Assessment We evaluate your unique risk profile and security requirements: model architecture, data pipelines, inference infrastructure, and third-party AI dependencies. Unlike generic security assessments, we focus on the questions your enterprise customers are actually asking.
Phase 2: Enterprise-Ready Security Design We design a security program that addresses both traditional enterprise requirements (SOC 2, access controls, incident response) and AI-specific concerns (data handling policies, model security, responsible AI governance, ISO 42001). The goal is a program you can confidently present to enterprise security teams.
Phase 3: Implementation Support Your fractional CISO helps you implement controls, prepare for audits, and respond to customer security questionnaires. We work alongside your engineering team to embed security into your ML pipelines without creating friction.
What You Need to Be Enterprise-Ready
What Enterprise Customers Expect from AI Vendors
Based on our work with AI startups selling to Fortune 500 companies, here's what you need to be enterprise-ready:
Table Stakes (Required):
- SOC 2 Type II or other certifications (or clear timeline to achieve it)
- Clear data handling policies - what you store, how long, who accesses it
- Customer data isolation - no training on customer data without explicit consent
- Incident response plan that covers AI-specific scenarios
- Vendor security documentation for your foundation model providers
Differentiators (Win Deals):
- Responsible AI policy with documented governance
- Model cards or documentation explaining capabilities and limitations
- Data lineage documentation showing training data provenance
- Red team testing results for adversarial AI attacks
- Clear stance on EU AI Act and emerging regulatory compliance
Advanced (Enterprise Premium):
- On-premise or VPC deployment options for sensitive customers
- Customer-managed encryption keys
- Dedicated model instances without shared infrastructure
- Audit logging of all model interactions
- Custom data retention and deletion capabilities
Common Questions
Do AI startups need different security than regular SaaS?
Yes and no. You need the same foundational security program - SOC 2, access controls, incident response, secure development. But you also face unique risks: model security, training data governance, prompt injection, and emerging AI regulations. Enterprise customers are asking AI-specific security questions that traditional SaaS vendors don't face. Your security program needs to address both.
How do we handle customer concerns about training on their data?
This is the most common enterprise objection to AI vendors. You need clear, documented policies: opt-in vs. opt-out for training, data isolation between customers, retention and deletion capabilities, and technical controls that enforce these policies. Many AI startups offer enterprise tiers with guaranteed data isolation and no-training commitments.
What's the timeline to get SOC 2 certified?
For AI startups with reasonable engineering practices, 4-6 months to SOC 2 Type I, then another 6 months of observation for Type II. We can often accelerate this by focusing on controls that matter most for your risk profile. Many enterprise customers will accept Type I plus a commitment to Type II, especially for innovative AI products they want access to.
How should we think about AI regulations like the EU AI Act?
Start with understanding your risk classification under the EU AI Act. Most B2B AI applications fall into limited or minimal risk categories, but some use cases (HR, credit, healthcare) may be high-risk. Document your compliance approach now - enterprise customers are already asking about regulatory readiness, and having a clear position differentiates you from competitors who haven't thought about it.