AI Security

AI security is the discipline of protecting AI systems themselves — the models, the infrastructure they run on, and the data pipelines feeding them — from adversarial attacks, unauthorized access, manipulation, and misuse. It covers threats specific to how AI systems work: adversarial inputs designed to mislead a model, data poisoning that corrupts what a model learns, model extraction attempts that try to steal a model's underlying parameters, and manipulation of a model's behavior through the content it processes.

This is a related but distinct discipline from AI Privacy, even though the two overlap and are often discussed together. AI privacy concerns what happens to personal and sensitive data across an AI system's lifecycle — training, prompts, logs. AI security concerns the integrity and defensibility of the system itself — whether it can be attacked, manipulated, or compromised, regardless of whether the specific data involved is personal or sensitive. A security failure can expose private data, which is where the two intersect, but a system can also be attacked or manipulated in ways that have nothing to do with personal information at all — corrupting a model's outputs, for instance, without necessarily exposing anyone's private data in the process.

A company deploying an AI agent with the ability to browse external content and take actions is a clear example of where AI security applies directly. That agent needs to be protected against prompt injection — malicious instructions hidden in content it processes that could redirect its behavior — as well as against broader manipulation of the tools and systems it has access to, since a compromised agent with real permissions can cause damage well beyond a single bad output.

The same discipline applies broadly wherever AI systems are deployed: a company training its own machine learning model needs to guard against data poisoning, where an attacker manipulates training data to corrupt the model's future behavior; an organization deploying a proprietary AI model needs to consider model extraction risk, where an attacker systematically queries the model to reconstruct or steal its underlying capabilities; and any organization using AI-powered decision systems needs to consider adversarial inputs specifically crafted to fool the model into a wrong classification or decision. In each case, AI security is focused on the system's own integrity and resistance to attack, a distinct concern from whether the data it processes is adequately protected.

Organizations that deploy AI systems without accounting for AI-specific security risks are exposed to a category of attack that traditional application security often doesn't cover. A conventional security review focused on access controls and encryption can miss threats unique to how AI systems actually work — a model can be technically well-secured from a traditional infrastructure standpoint and still be vulnerable to adversarial manipulation, data poisoning, or extraction, because those threats target the AI system's behavior, not just its access controls.

Treating AI security as covered by conventional application security is a mismatch — the threats specific to how AI systems learn, reason, and act require their own evaluation beyond standard infrastructure controls.

AI security spans a broad set of concerns — adversarial inputs, model extraction, data poisoning — that sit largely outside what Questa AI is built to address. Questa's entity-detection engine performs local redaction and masking of sensitive identifiers before data reaches an AI model, which addresses the specific intersection between AI security and data protection: reducing what a security failure, a manipulated agent, or a compromised system could actually expose, since the sensitive data was never transmitted to it in an identifiable form in the first place.

This matters specifically for Safe AI Agents and multi-step agentic workflows, where Questa anonymizes sensitive data at every step an agent touches — meaning that even if an agent's behavior were manipulated through something like prompt injection, the sensitive data it could expose as a result is limited by what was masked before it ever reached that step. Broader AI security concerns — securing the model's training process, defending against extraction, hardening the underlying infrastructure — remain separate work outside what a data protection layer determines, but the data-exposure consequence of an AI security failure is directly reduced by what Questa protects before transmission.