AI Security · Agentic AI Runtime Protection
Protect AI Agents Across Prompts, Data and Tool Actions
Votal secures enterprise AI agents and LLM applications at runtime with guardrails, agent identity, data policies and tool authorisation. It evaluates prompts, model outputs and agent actions in real time to help enforce how AI systems can access data and connected tools.
The AI Attack Surface Your Security Team Isn’t Watching Yet
AI assistants and agents can accept instructions from users, connected data and external systems, then act through tools or business applications. Prompt injection and goal manipulation can influence these workflows in ways that traditional network controls may not fully understand. Runtime guardrails add application-level context around prompts, outputs and agent actions.
Why FVC + Votal
As organisations adopt AI agents and LLM-powered workflows, runtime controls become part of the application-security architecture. FVC positions Votal as a dedicated AI-security layer for governing prompts, data handling, agent identity and tool actions across enterprise deployments.
Target Sectors
Key Capabilities
Stop Attacks Before They Reach Your Model
Prompt injection is the SQL injection of the AI era. An attacker crafts an input that overrides the AI’s instructions causing it to reveal sensitive data, bypass access controls, or perform unauthorized actions. Votal intercepts every input before it reaches your LLM, analyses it in real time, and blocks malicious prompts before they execute. Your AI application behaves as intended not as an attacker intends.
Technical Note
Real-time prompt analysis and classification for jailbreaks, indirect prompt injection and adversarial patterns, with low-latency runtime enforcement.
What Your AI Knows, It Shouldn’t Always Say
Enterprise LLMs are trained on or connected to internal data contracts, customer records, financial data, internal policies. Without output controls, a well-crafted prompt can extract that data from a user who was never meant to have access. Votal monitors every LLM output, applies data classification rules, and blocks or redacts sensitive information before it reaches the user enforcing your data governance policies at the AI layer.
Technical Note
Output classification and sensitive data detection. Configurable redaction and blocking policies by data category. Audit log of all flagged outputs for compliance reporting.
Test Your AI’s Defences, Automatically
Your AI security posture degrades every time the model is updated, fine-tuned, or connected to a new data source. Manual red teaming is too slow and too infrequent to keep pace. Votal continuously probes your deployed LLMs with adversarial inputs simulating real attack techniques and surfaces new vulnerabilities as they emerge. Your security team sees what attackers would try, before they try it.
Technical Note
Automated adversarial testing against deployed LLM endpoints. Coverage of OWASP LLM Top 10 attack categories. Continuous reporting on AI security posture drift.
Deploy AI with confidence, not hope
Your AI applications deliver business value without becoming breach pathways. Prompt injection attacks are blocked before they execute. Sensitive data stays inside your governance perimeter. And when your regulator, auditor, or board asks how you’re securing your AI deployments, you have a complete answer not a promise to look into it. Runtime guardrails. Policy enforcement.
Continuous red teaming. AI you can deploy with confidence.
Technical Specifications
Trust & Credibility Signals
