Cisco is taking a radical approach to AI security in its new AI Defense solution.
In an exclusive interview Sunday with Rowan Cheung of The Rundown AI, Cisco Executive Vice President and CPO Jeetu Patel said that AI Defense is “taking a radical approach to address the challenges that existing security solutions are not equipped to handle.”
AI Defense, announced last week, aims to address risks in developing and deploying AI applications, as well as identifying where AI is used in an organization.
AI Defense can protect AI systems from attacks and safeguard model behavior across platforms with features such as:
Detection of shadow and sanctioned AI applications across public and private clouds;
Automated testing of AI models for hundreds of potential safety and security issues; and
Continuous validation safeguards against potential safety and security threats, such as prompt injection, denial of service, and sensitive data leakage.
The solution also allows security teams to better protect their organizations’ data by providing a comprehensive view of AI apps used by employees, create policies that restrict access to unsanctioned AI tools, and implement safeguards against threats and confidential data loss while ensuring compliance.
“The adoption of AI exposes companies to new risks that traditional cybersecurity solutions don’t address,” Kent Noyes, global head of AI and cyber innovation at technology services company World Wide Technology in St. Louis, said in a statement. “Cisco AI Defense represents a significant leap forward in AI security, providing full visibility of an enterprise’s AI assets and protection against evolving threats.”
Positive Step for AI Security
MJ Kaufmann, an author and instructor at O’Reilly Media, operator of a learning platform for technology professionals, in Boston, affirmed Cisco’s analysis of existing cybersecurity solutions. “Cisco is right,” she told TechNewsWorld. “Existing tools fail to address many operationally driven attacks against AI systems, such as prompt injection attacks, data leakage, and unauthorized model action.”
“Implementers must take action and implement targeted solutions to address them,” she added.
Cisco is in a unique position to provide this kind of solution, noted Jack E. Gold, founder and principal analyst at J.Gold Associates, an IT advisory company in Northborough, Mass. “That’s because they have a lot of data from their networking telemetry that can be used to reinforce the AI capabilities they want to protect,” he told TechNewsWorld.
Cisco also wants to provide security across platforms — on-premises, cloud, and multi-cloud — and across models, he added.
Concerning Limitations
Although Cisco’s approach of embedding security controls at the network layer through their existing infrastructure mesh shows promise, it also reveals concerning limitations, maintained Dev Nag, CEO and founder of QueryPal, a customer support chatbot based in San Francisco.
“While network-level visibility provides valuable telemetry, many AI-specific attacks occur at the application and model layers that network monitoring alone cannot detect,” he told TechNewsWorld.
“The acquisition of Robust Intelligence last year gives Cisco important capabilities around model validation and runtime protection, but their focus on network integration may lead to gaps in securing the actual AI development lifecycle,” he said. “Critical areas like training pipeline security, model supply chain verification, and fine-tuning guardrails require deep integration with MLOps tooling that goes beyond Cisco’s traditional network-centric paradigm.”
“Think about the headaches we’ve seen with open-source supply chain attacks where the offending code is openly visible,” he added. “Model supply chain attacks are almost impossible to detect by comparison.”
Nag noted that from an implementation perspective, Cisco AI Defense appears to be primarily a repackaging of existing security products with some AI-specific monitoring capabilities layered on top.
“While their extensive deployment footprint provides advantages for enterprise-wide visibility, the solution feels more reactive than transformative for now,” he maintained. “For some organizations beginning their AI journey that are already working with Cisco security products, Cisco AI Defense may provide useful controls, but those pursuing advanced AI capabilities will likely need more sophisticated security architectures purpose-built for machine learning systems.”
