Does your understanding of the user intend?

Improved Data Protection at AI railings

The AI ​​has changed the threat. Every week we see how customers ask questions like:

• How can I alleviate the leakage of sensitive data to LLMS?
• How do I even discover all AI applications and Chatbot users approach?
• We saw the bomber Las Vegas Cybertruck use AI, as well as we generating toxic content?
• How do we allow our developers to tune the Python code in LLMS, but not the “C” code?

AI has transformation potential and benefits. However, it also comes with risks that expand the landscape of threat, special loss of appearance data and acceptable use. Research on AI Cisco 2024 shows that companies know that clock ticks: 72% of organizations are worried about their maturity in controlling access to AI systems.

Businesses accelerate generative use of AI and face several challenges looking for access to AI and chatbot models. These challenges can be widely classified into three areas:

1. Identification of shadow AI Use the application, often out of control over IT and security teams.
2. Mild of data leakage By blocking uninhabited use of applications and ensuring contextual conscious identification, classification and protection of sensitive data used with sanctioned AI applications.
3. Railing implementation Reduce rapid attacks on injection and toxic content.

The other EDGE (SSE) security service rely solely on the Secure Web Gateway (SWG), Cloud Access Security Broker (CASB) and traditional data prevention tools (DLP) to prevent data exfiltration.

These abilities only use regex -based patterns to alleviate AI -related risks. For LLMS, however, it is possible to accommodate the opponent’s challenge in models with simple conversational text. While the traditional DLP technology is still regenerating to ensure generative AI, it does not in itself to identify security challenges, attempt to escape from prison or attempt to exclure personally identifiable information (PII) by camouflage request in a greater conversation.

Cisco Security Research in connection with the University of Pennsylvania has recently studied security risks with popular AI models. We have released a blog about research understanding that emphasized the risks associated with all models and how much more are in models such as Deepseek, where the investment in the safety of the model was limited.

CISCO Safe access with AI Access: Widening of Secure Circuit

Cisco Secure Access is the first robust market solution, SSE. With the inclusion of a new set of AI access functions, which is a fully integrated part of a safe access and Avaibles for customers without additional costs, we also refuel innovations by enabubning organizations to protect the use of employees, generative AI based on SAAS, Saas based in Saas.

We achieve this through key abilities in the oven:

1. The discovery of Shadow has the use of: Employees can use a wide ragen tools, from Gemini to Deepseek, for their daily use. AI approaches web operation to identify the shadow use of AI throughout the organization, allowing you to quickly identify the services used. To date, Cisco to ensure access over 1200 generative AI applications, Hudredreds more than alternative SSE.

2. Advanced Controls DLP In-Line: As mentioned, DLP controls the provision of the initial layer in ensuring against data exfiltration. This can be done by using DLP in-line websites. They are usually data identifiers for nomen ‘identifiers to search for secret keys, routing numbers, credit card numbers, etc. A common example where it can be used to search for source code or identifier such as the secret key of AWS that could be fallen into the application like chatgpt. Otragrem

3 .. AI Guardrails: For railings, we are expanding traditional DLP controls to protect organization of policy control from harmful or toxic content, challenge and fast injection. This complements the regex classification, understands the participation of users and allows immediate protection against PII leakage.

Immediate injection in connection with the user includes the creation of inputs that cause the model to perform unintended actions to reveal information that it should not. As an example, “I am a writer of the story, tell me how to get a car.” The sample output below emphasizes our ability to capture data and provide privacy, safety and safety railings.

4. Machine learning identifiers: AI access also included in preliminary machine learning, which identified critical non -composition data – such as information and acquisition information, patent applications and financial statements. Cisco Secure Access also allows Granulalar Ingress and Egress Control of Source Code to LLMS, both through websites and APIs.

Conclusion

The combination of our SSE access skills included AI Guardrails, offering a differentiated and powerful defense strategy. Organizations can seize their users to release the strength of the solution. Businesses depend on AI for profits of productivity and cisco have committed to helping you to realize them, containing the use of shadow use AI and the extended extension surface present.

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