AI Security & Model Protection

AI security is the newest frontier in cybersecurity: protecting AI systems themselves. As organizations deploy large language models, agents, and AI applications into production — often with access to data and the ability to take actions — these systems become high-value targets and introduce entirely new categories of risk. Securing them is a distinct discipline from traditional cybersecurity, and a fast-growing one, because the attack surface and failure modes are unlike anything before.

AI systems can be manipulated through prompt injection, where hidden instructions hijack a model's behavior; through data poisoning that corrupts what a model learns; and through attacks that extract sensitive training data or jailbreak safety controls. As AI agents gain the ability to act — moving money, running code, accessing systems — the stakes rise sharply. A new layer of tools secures AI applications, monitors model behavior, and red-teams systems for these novel weaknesses. The discipline is defining itself in real time as AI deployment outpaces the security around it.

A distinct sub-discipline has formed around non-human identity: the credentials that belong to software rather than people. Service accounts, tokens, digital keys and AI agents all authenticate and act, and they now vastly outnumber human accounts inside most enterprises — but they are provisioned casually, rarely rotated, and often over-permissioned, which makes them an attractive path for an attacker. Agents sharpen the problem, because an agent that is handed broad credentials can take a long chain of actions before anyone reviews the first one. The market has priced this in: in July 2026 the data-security company Cyera, valued at $12 billion a month earlier, signed a letter of intent to acquire the non-human identity specialist Oasis Security for $1 billion. Practical controls look familiar in outline and different in detail — inventory every non-human identity, scope each to least privilege, rotate and expire credentials automatically, and log agent actions in a form a human can audit after the fact.

Cisco AI Defense secures enterprise AI applications — protecting models and agents against prompt injection, data leakage, and misuse — and Cisco Secure AI Factory with NVIDIA builds security into the infrastructure that runs AI workloads. The assistants Claude and ChatGPT help security teams understand AI threats and design defenses, even as they are also the kind of system being protected. This is a young category, and the tooling is evolving as fast as the threats.

AI security is among the fastest-emerging specialties in all of technology, because it barely existed a few years ago and is now essential as AI moves into production everywhere. The opportunity is large for security professionals who learn it: the skills are scarce, the demand is exploding, and the problems — securing systems that are probabilistic, manipulable, and increasingly autonomous — are genuinely new. The stakes climb as AI agents gain real-world capabilities, since a compromised agent can act on its access. This is the security frontier most specific to the AI era, and it is where defending AI and the broader project of building trustworthy, safe AI systems most directly meet.