AI Security – Identify risks. Stay in control.
Generative AI and large language models (LLMs) are increasingly being integrated into business processes, ranging from developer tools to knowledge management and autonomous AI agents.
However, these technologies give rise to new security risks. Unlike traditional IT systems, LLMs do not clearly distinguish between data and instructions. As a result, content can appear manipulative and influence AI systems.
Companies must therefore adapt their security architecture and governance to this new technology.
Common AI Security Risks
- Shadow AI
Employees use external AI tools and inadvertently disclose sensitive data. - Prompt Injection
Attackers manipulate AI systems through input or documents. - Data breaches
AI systems can disclose confidential information from internal knowledge sources. - Overprivileged AI agents
AI systems perform unwanted actions in connected systems.
How to Use AI Safely
A secure AI strategy combines architecture, technical safeguards, and governance.
| Security approach | Benefits |
|---|---|
| Zero-Trust Architecture | Access controls outside the AI model |
| AI Firewalls and Filtering | Protection against prompt injection |
| DLP and Data Masking | Protection of sensitive data |
| Model Hardening | Protecting AI Infrastructure |
| AI Monitoring | Early detection of attacks |
AI Security with Asecus
Asecus helps companies securely implement AI systems, from strategy and architectural design to implementation. Contact us today to book an AI Security Workshop.
AI Security (AISEC)
/in AI Security, Products /by Dana BadulescuCato AI Security for Applications protects in-house AI applications and AI agents in enterprises from attacks during runtime. The goal is to detect and stop risks before they impact users, systems, or data. The aim is to enable enterprises to operate their own AI apps securely, without attacks on models, data, or users having any impact.
Security mechanisms monitor the communication and behavior of AI apps to detect attacks early on.
These include, for example, input manipulation (prompt attacks), data exfiltration, or the misuse of AI functions.
The solution operates while the AI application is in use and prevents attacks in real time.
The security features are integrated into Cato’s cloud-based platform and operate with low latency.
AI-powered analytics are designed to keep the number of false security alerts low.
Cato AI Security for End Users safeguards employees’ use of AI tools (such as chatbots, copilots, or other AI services). It provides transparency and control over all AI interactions within the organization. The goal is to ensure the secure and controlled use of generative AI tools within the organization, without data leaks or compliance issues.
Identifies AI tools that employees use without official approval.
Organizations can see which AI apps are being used and what data is being sent to them.
Security policies can specify which AI tools are permitted and what data may be shared.
Every interaction with AI services is monitored and evaluated to minimize risks.
IT teams can analyze usage, assess risks, and enforce security measures.
Prisma AIRS
/in AI Security, Products /by Dana BadulescuPalo Alto Networks Prisma AI Runtime Security protects AI applications, models, and data while they are in operation. It monitors AI systems in real time and prevents common AI attacks such as prompt injection, malicious code, data leaks, or model misuse. It also detects risky content, resource overload, or manipulated responses, and protects AI agents from attacks such as identity impersonation or tool abuse. The goal is the secure development and use of LLM-based applications in enterprise environments. The AI Red Teaming Agent performs automated penetration tests on your AI applications and models. It subjects your AI implementations to a stress test, learning and adapting just like a real attacker.
Palo Alto Networks AI Access Security protects organizations when employees use generative AI tools. The solution provides visibility into which AI apps are being used, controls access, and prevents data leaks or malicious content in prompts and responses. This enables organizations to use GenAI securely while reducing risks such as the unintentional disclosure of sensitive data. The goal is the secure use of external AI services and GenAI tools in everyday work.