AI SECURITY FOR TECHNOLOGY
AI Security Assessment for Technology
Govern AI across engineering, product, and operations as shadow AI scales with your workforce.
Tech Companies Build AI Products — But Who Secures the AI They Use?
Engineers paste proprietary code into AI assistants for debugging. Product teams integrate AI APIs without security review. Customer data flows through AI-powered features that were shipped faster than governance could evaluate. In technology companies, the very culture of rapid iteration and developer autonomy that drives innovation also creates the conditions for shadow AI to proliferate at a scale no other industry matches.
The adoption numbers tell the story. The 2025 Stack Overflow Developer Survey found that 84% of developers use or plan to use AI tools in their development process, with 51% using them daily. IBM's 2025 Cost of a Data Breach Report found that 63% of breached organizations either lack an AI governance policy or are still developing one — and that organizations with high levels of shadow AI face $670,000 in additional breach costs per incident. For technology companies where every engineer is a potential AI adopter, the surface area is enormous.
Technology companies face a unique double exposure: they must secure both the AI they build into products and the AI their teams use internally. The average cost of a technology sector data breach reached $4.79 million in 2025, and with SOC 2 auditors, enterprise customers, and regulators increasingly scrutinizing AI governance, companies that treat AI security as an afterthought risk losing both customer trust and competitive position.
Regulatory & Compliance Landscape
SOC 2
SOC 2 trust service criteria are the baseline for technology companies handling customer data. AI systems that process, generate, or access customer information must meet SOC 2 requirements — and auditors are increasingly examining AI governance as part of the security and confidentiality criteria.
ISO 27001
ISO 27001 provides the information security management system framework that technology companies use to demonstrate security maturity. Its Annex A controls now must account for AI-specific risks including model security, data classification, and AI supply chain management.
EU AI Act
For technology companies selling into the EU, the AI Act classifies AI systems by risk level and imposes requirements for high-risk systems including conformity assessments, transparency obligations, and technical documentation — directly affecting AI-powered product features.
NIST AI RMF
The NIST AI Risk Management Framework helps technology companies structure their approach to AI risk — from identifying risks in AI-powered products to governing internal AI tool usage across engineering, operations, and customer-facing systems.
OWASP AI Security
OWASP's AI security guidance addresses the specific attack vectors technology companies face — prompt injection, model theft, training data poisoning, and insecure AI plugin design — providing actionable controls for securing AI-powered applications.
What We Assess in Technology
Shadow AI at Engineering Scale
Identify and catalog AI tools adopted by engineering teams without security review — code assistants, debugging tools, documentation generators — and evaluate data exposure, access controls, and governance gaps.
AI in SDLC & Code Generation
Assess AI tools integrated into the software development lifecycle for code generation, review, and testing — including intellectual property risks, code quality safeguards, and security of AI-suggested code.
Model Security & Prompt Injection
Evaluate the security posture of AI models deployed in products, covering prompt injection defenses, model access controls, output filtering, and protections against adversarial inputs and data extraction attacks.
AI API Sprawl
Map and assess the growing landscape of AI API integrations across products and internal tools — evaluating authentication, rate limiting, data exposure, vendor security posture, and cost controls.
Customer Data in AI Features
Review how customer data flows through AI-powered product features, assessing data isolation, processing transparency, opt-out mechanisms, and compliance with customer data processing agreements.
HOW IT WORKS
From Sign-Up to Secure in Three Steps
Connect Your Network
Upload firewall or DNS logs, or deploy our lightweight Docker collector. No agents on endpoints. We read metadata only — never your data.
See Every AI Tool
Within minutes, see a complete inventory of AI tools in use across your organization. Set policies: approved, monitored, or restricted.
Prove Compliance
Run assessments against 11 compliance frameworks. Get AI-powered remediation playbooks, track progress over time, and download audit-ready reports.
Transparent Pricing. Start Free.
Free for your first AISS assessment. Pro $1,200/yr for a paid framework. Business $3,600/yr for the full compliance suite. Enterprise from $15,000/yr — published floor, never hidden.
Glass-Box scoring
Every category score is fully derivable from your answers and the published AISS methodology. Your auditor can reproduce the math from the public spec alone.
Open standard
AISS is published under CC-BY-4.0 at github.com/Ayliea/aiss. Fork it, audit it, or propose changes via the public RFC process — the standard belongs to the practitioner community.
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