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What is Cisco AI Defense?

Cisco AI Defense serves as a comprehensive security framework designed to empower organizations to safely develop, deploy, and utilize AI technologies. It effectively addresses critical security challenges, such as shadow AI, which involves the unauthorized use of third-party generative AI tools, while also improving application security through enhanced visibility into AI resources and implementing controls that prevent data breaches and minimize potential threats. Key features of this solution include AI Access for managing third-party AI applications, AI Model and Application Validation that conducts automated vulnerability assessments, AI Runtime Protection offering real-time defenses against adversarial threats, and AI Cloud Visibility that organizes AI models and data sources across diverse distributed environments. By leveraging Cisco's expertise in network-layer visibility and continuous updates on threat intelligence, AI Defense ensures robust protection against the evolving risks associated with AI technologies, thereby creating a more secure environment for innovation and advancement. Additionally, this solution not only safeguards current assets but also encourages a forward-thinking strategy for recognizing and addressing future security challenges. Ultimately, Cisco AI Defense is a pivotal resource for organizations aiming to navigate the complexities of AI integration while maintaining a solid security posture.

What is Antares?

Antares is a collection of open-weight security small language models crafted to detect vulnerabilities within large codebases. Featuring models such as Antares-350M and Antares-1B, these tools can be deployed locally or on-site, ensuring that proprietary source code remains secure while also reducing both inference expenses and runtime. The procedure starts with an outline of the vulnerability, which may include an advisory or a CWE category; from there, the model embarks on a detailed investigation similar to that of a human analyst, methodically looking for relevant code patterns, scrutinizing possible files, integrating new data, and adjusting its strategy when certain paths appear unproductive. This method allows the model to concentrate its resources on the files most likely to contain the identified flaws. In the end, Antares produces a prioritized list of source files that may be vulnerable, accompanied by a comprehensive trail of the exploration process that led to these conclusions, thereby simplifying the review and prioritization for teams. Furthermore, this functionality not only accelerates the vulnerability assessment process but also significantly strengthens the overall security framework of the development environment, fostering a culture of proactive security measures. Ultimately, organizations can benefit from improved efficiency and effectiveness in managing their code vulnerabilities.

Media

Media

Integrations Supported

Agent Control

Integrations Supported

Agent Control

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Cisco

Date Founded

1984

Company Location

United States

Company Website

www.cisco.com/site/us/en/products/security/ai-defense/index.html

Company Facts

Organization Name

Cisco

Date Founded

1984

Company Location

United States

Company Website

blogs.cisco.com/ai/introducing-antares-the-most-efficient-open-weight-ai-models-for-vulnerability-localization

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