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Built for the enterprise, so enterprises can confidently develop, deploy, and secure AI applications.News Highlights:
Cisco’s end-to-end solution protects the development and use of AI applications, so enterprises can move forward with their AI initiatives with confidence.AI Defense protects against the misuse of AI tools, data breaches, and increasingly sophisticated threats that existing security solutions can’t handle.This innovative solution leverages Cisco’s unmatched network visibility and control to stay ahead of evolving AI security and safety issues.SAN JOSE, Calif., January 15, 2025 — Cisco (NASDAQ: CSCO), a leader in security and networking, today announced Cisco AI Defense, a breakthrough solution that enables and protects AI transformation within the enterprise. As AI advances, new security issues and security threats emerge at an unprecedented rate, and existing security solutions can’t keep up. Cisco AI Defense is built for enterprises, helping them confidently develop, deploy, and secure AI applications.
“When embracing AI, business and technology leaders cannot sacrifice security for speed. In a competitive, fast-changing environment, speed makes the difference. Built into the fabric of the network, Cisco AI Defense combines unique capabilities to detect and defend against threats as AI applications are developed and accessed, without the need to make trade-offs,” said Jeetu Patel, executive vice president and chief product officer at Cisco.
The risk of AI going wrong is extremely high. According to Cisco’s 2024 AI Readiness Index, only 29% of respondents believe they are fully capable of detecting and preventing unauthorized AI tampering. Because AI applications are multi-model and multi-cloud, the security challenges are also new and complex. Vulnerabilities can occur at the model or application level, and responsibility falls on different owners, including developers, end users, and vendors. As enterprises move beyond public data and begin training models on proprietary data, the risks only increase.
To unlock AI innovation and adoption, enterprises need a universal security layer to protect every user and every application. AI Defense supports the AI ​​transformation of enterprises by addressing two pressing risks:
Develop and deploy secure AI applications: As AI becomes ubiquitous, enterprises will use and develop hundreds or even thousands of AI applications. Developers need a set of AI security safeguards that apply to each application. AI Defense helps developers move fast and unlock greater value by protecting AI systems from attacks and securing model behavior across platforms. AI Defense capabilities include:
Discover AI: Security teams need to understand who is building applications and what training sources they use. AI Defense detects shadow AI applications and sanctioned AIapplications in public and private clouds.
Model validation: Model tuning can lead to harmful and unexpected results. Automated testing checks AI models for hundreds of potential security issues. This AI-driven algorithmic red team identifies potential vulnerabilities and recommends guardrails in AI Defense for security teams to use.
Runtime security: Continuous validation provides ongoing protection against potential security threats such as tip injection, denial of service, and sensitive data leakage.Securing access to AI applications: As end users adopt AI applications such as summarization tools to increase productivity, security teams need to prevent data breaches and poisoning of proprietary data. AI Defense provides security teams with the following capabilities:
Visibility: Provides a comprehensive view of shadow and approved AI applications used by employees.
Access control: Enforces policies that limit employee access to unapproved AI tools.
Data and threat protection: Continuously protect against threats and loss of confidential data while ensuring compliance.
Unlike security guardrails built into individual AI models, Cisco provides consistent controls for a multi-model world. AI Defense is self-optimizing, leveraging Cisco’s proprietary machine learning models to detect evolving AI security issues based on threat intelligence data from Cisco Talos. Splunk customers using AI Defense will receive enriched alerts and more context from across the ecosystem. AI Defense seamlessly integrates with existing data flows, provides unparalleled visibility and control, and is built into Cisco’s unified AI-driven cross-domain security platform, Security Cloud. It leverages Cisco’s extensive network of enforcement points to enforce AIsecurity at the network level in a way that only Cisco can provide. Accuracy and trustworthiness are critical to protecting enterprise AI applications, and Cisco has been actively involved in setting industry standards for AI security, including those from MITRE, OWASP, and NIST.

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‍‍‍‍ As 2024 draws to a close, venture capitalist Rob Toews from Radical Ventures shares his 10 predictions for AI in 2025:
01. Meta will start charging for Llama models
Meta is the world benchmark for open AI. In a compelling case study in corporate strategy, Meta has chosen to make its state-of-the-art Llama model available for free, while competitors like OpenAI and Google have closed sourced their cutting-edge models and charged for use.
So the news that Meta will start charging companies to use Llama next year will come as a surprise to many.
To be clear: we are not predicting that Meta will completely close source Llama, nor are we predicting that anyone using the Llama model will have to pay for it.
Rather, we are predicting that Meta will make the terms of Llama’s open source license more stringent so that companies above a certain size that use Llama in a commercial context will need to start paying to use the model.
Technically, Meta already does this today to a limited extent. The company doesn’t allow the largest companies — cloud supercomputers and other companies with more than 700 million monthly active users — to use its Llama models for free.
As early as 2023, Meta CEO Mark Zuckerberg said: “If you’re a company like Microsoft or Amazon or Google, and you’re basically reselling Llama, then we should get some revenue from it. I don’t think there will be a lot of revenue in the short term, but in the long term, hopefully there will be some revenue.”
Next year, Meta will significantly expand the range of companies that must pay to use Llama to include more large and medium-sized companies. Keeping up with the cutting edge of large language models (LLMs) is very expensive. Meta needs to invest billions of dollars each year to keep Llama consistent or close to consistent with the latest cutting-edge models from companies like OpenAI, Anthropic, and others.
Meta is one of the largest and best-funded companies in the world. But it’s also a public company and ultimately accountable to shareholders.
As the cost of making cutting-edge models continues to soar, it becomes increasingly untenable for Meta to invest so much money to train the next generation of Llama models without revenue expectations.
Over the next year, Llama models will continue to be available for free to enthusiasts, academics, individual developers, and startups. But 2025 will be the year Meta starts to seriously make Llama profitable.
02. Questions about “scaling laws”
In recent weeks, the most discussed topic in the field of artificial intelligence has been scaling laws, and the question of whether they are about to end.
Scaling laws were first proposed in an OpenAI paper in 2020. Its basic concept is simple and straightforward: when training an artificial intelligence model, as the number of model parameters, the amount of training data, and the amount of computation increase, the model’s performance will improve in a reliable and predictable way (technically, its test loss will decrease).
From GPT-2 to GPT-3 to GPT-4, the amazing performance gains are attributed to scaling laws.
Like Moore’s Law, scaling laws are not actually real laws, but just empirical observations.
In the past month, a series of reports have shown that major artificial intelligence labs are experiencing diminishing returns as large language models continue to scale up. This helps explain why OpenAI’s GPT-5 release has been repeatedly delayed.
The most common rebuttal to the stagnation of scaling laws is that the advent of test-time computation has opened up a whole new dimension in the pursuit of scaling.
That is, new inference models like OpenAI’s o3 can massively scale computation during inference, unlocking new AI capabilities by letting models “think longer”, rather than massively scaling computation during training.
This is an important point. Test-time computation does represent an exciting new avenue to achieve scaling and AI performance gains.
But another point about scaling laws is even more important, and one that is severely underappreciated in today’s discussion. Almost all discussions of scaling laws, starting with the original 2020 paper and continuing through today’s focus on test-time computation, have focused on language. But language is not the only data modality that matters.
Think about robotics, biology, world models, or networked agents. For these data modalities, scaling laws have not yet saturated; rather, they are just beginning.
In fact, rigorous proofs of scaling laws for these fields have not even been published to date.
Startups building foundational models for these new data patterns (e.g., evolutionary scaling in biology, physical intelligence in robotics, world labs in world models) are trying to identify and exploit scaling laws in these fields, just as OpenAI successfully exploited scaling laws for large language models (LLMs) in the first half of the 2020s.
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