SaaS companies are embedding copilots, RAG assistants and AI agents directly into products that process customer data and connect to customer systems. As these AI features gain access to tools, integrations and multi-tenant data, AI security becomes part of both product security and customer trust.
Cygeniq helps technology companies test AI features against real-world attacks, enforce runtime guardrails, discover AI assets, govern AI risk and detect AI-native threats through one Runtime AI Trust Platform.
One platform across the AI you build and ship
Traditional application security testing remains important, but AI applications introduce additional attack paths.
Prompt injection can manipulate AI behavior. Retrieval failures can expose information across tenants. Agents with tool access can perform unintended actions. Every new model, prompt, connector or integration can also change the AI security surface.
Existing application security testing already covers the AI feature, so a copilot or agent is just another part of the product surface.
A prompt, a retrieval source and an agent's tool access are all new attack paths that standard application security testing was never built to reach.
This creates risks including:
SaaS AI security needs to protect the model and the complete system around it, including prompts, retrieval, agents, tools, connectors and customer data.
Retrieval or scoping failures can expose one customer's data to another.
AI agents may act within customer environments using delegated authority.
Connectors expand what a manipulated AI instruction can access.
External models, adapters and proxies introduce inherited dependencies and risk.
Enterprise buyers increasingly expect evidence showing how AI features are tested, governed and protected.
One connected sequence, from discovery through defense, mapped to the modules that deliver each step.
Run adversarial testing for cross-tenant extraction, prompt injection, unsafe tool use and connector abuse.
Apply runtime monitoring and guardrails across tenants and retest as models, prompts, connectors or policies change.
Maintain an AI bill of materials across models, prompt templates, retrieval corpora, connectors and agents with ownership and tenant scope.
Assess AI risk based on factors such as tenant reach, data access and tool authority.
Connect AI assets with controls and evidence to support governance, audit and enterprise security reviews.
Provide visibility and traceability for AI-layer attacks across SaaS and digital-platform environments.
AI-accelerated exploitation of your platform, AI phishing and account takeover of customer admins, brand and domain impersonation, and autonomous intrusions, detected, shielded and contained, with adversarial-evasion resistance as attackers adapt.
Book a walkthrough of discovery, adversarial testing, runtime protection and AI-native detection for SaaS AI.
| AI Use Case | Key AI Security Risk |
|---|---|
| In-Product Copilots | Prompt injection and customer-data exposure |
| Multi-Tenant RAG | Cross-tenant information leakage |
| AI Agents | Tool misuse and excessive authority |
| MCP & Integrations | Expanded attack reach through connectors |
| Customer Uploads | Indirect prompt injection |
| Model Supply Chain | Inherited model and dependency risk |
| Enterprise AI Features | Insufficient security and governance evidence |
Controls and evidence map once, in GRCortex AI, to what enterprise buyers and regulators ask a technology company for: SOC 2, ISO/IEC 27001 and ISO/IEC 42001, the EU AI Act's obligations on providers and deployers (including general-purpose AI), the NIST AI RMF and GDPR, with technical testing aligned to the OWASP LLM and Agentic Top 10 and MITRE ATLAS, the frameworks enterprise security questionnaires now cite.
Continuous adversarial red teaming from a 1M+ scenario library, model validation and drift monitoring for every model, copilot and agent in scope, with findings flowing into GRCortex AI as evidence and into CyberTix AI as detection context.
Dynamic AI risk register fed by runtime findings, controls mapped once across every framework in scope, continuous control monitoring, audit-ready evidence and board-ready AI assurance.
The runtime security layer for enterprise AI. CyberTix Secure discovers every LLM, RAG pipeline and agent, blocks AI-native attacks inline and contains threats in real time. CyberTix Defend stops AI phishing and BEC, deepfakes, AI malware and autonomous intrusions, one backbone, one AI-risk picture.
Discover → Understand → Validate → Protect → Govern → Monitor → Assure → Improve
A digital-platform customer runs Cygeniq across content-management AI with bias and adversarial testing, improper-output monitoring and anomaly detection, robust AI defense, protected brand reputation and reduced operating cost.
Reference calls available under NDA.AI security for SaaS protects AI-powered product features such as copilots, RAG systems and agents from AI-specific attacks, data exposure and unsafe behavior.
Teams should control tenant access, test retrieval isolation, apply adversarial testing and monitor AI behavior at runtime.
Traditional application security remains necessary, but AI introduces additional risks such as prompt injection, retrieval poisoning and unsafe agent actions.
Agents can interact with tools and customer systems using delegated permissions, creating risks from excessive authority and manipulated instructions.
Cygeniq combines adversarial testing, runtime protection, AI inventory, risk governance and AI-native attack detection.
GRCortex AI helps maintain AI inventory, controls and evidence while Hexashield AI provides adversarial testing and runtime assurance.
Yes. CyberTix AI sees live tool calls and MCP traffic, enforces on the agent's actions with least-privilege non-human identity, and Hexashield AI red-teams connector abuse before release.
No. Cygeniq adds AI-specific security around existing product and application security programs.
Protect AI features as models, prompts, agents and integrations evolve with every release.
Cygeniq helps you secure AI systems, govern AI risk and prove control.
AI Trust Infrastructure for secure, governed and accountable enterprise AI
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