Why traditional tools are missing?
Most legacy security tools were built to protect deterministic systems, environments where software follows predictable logic and outcomes. Inputs are defined and the team can reasonably expect output. AI systems, particularly generative and agents, learn from data that is often derived from dynamic, unique or external sources, allowing attackers to tamper with the learning process. Techniques like data addiction allow malicious actors to subtly manipulate training data to produce harmful results later. This allows the AI model to be misused through rapid injection even after training, not after making the dish, but rather tampering with ingredients in the recipe. These attacks embed malicious instructions in seemingly innocent inputs and redirected the behavior of the model without system-level compromise. Agent AI that can act autonomously poses even greater risk. Imagine an AI assistant reading a website with secret commands embedded in it. You may take unauthorized actions without purchasing, missing information or detecting it. These are just a few examples. Traditional web app scanners, antivirus tools, and SIEM platforms have not been built for this reality. For the AI world, it’s more than just a best practice. it’s necessary. For AI, design-safe means integrating protection in the lifecycle of Machine Learning Security Operations (MLSECOPS) from initial scoping, model selection, data preparation to training, testing, deployment and monitoring. It also means adapting classic security principles of confidentiality, integrity, and availability (CIA) to fit the AI-specific context.
New toolset for AI security
A robust security attitude requires hierarchical defenses that take into account each phase of the AI pipeline and predict how AI systems will be manipulated directly and indirectly. Here are some categories to prioritize:
1. Model scanner and red team.
Static scanners look for backdoors, embedded biases, and insecure output in model code or architecture. Dynamic tools simulate adversarial attacks to test runtime behavior. These will be complemented by AI’s red teaming. Testing for injection vulnerabilities, model extraction risk, or harmful emergency behavior.
2. AI-specific vulnerability feed.
Traditional CVEs do not capture the rapidly evolving threats with AI. Organizations need a model architecture, new rapid injection patterns, and real-time feeds that track data supply chain risk vulnerabilities. This information helps you prioritize AI-specific patching and mitigation strategies.
3. AI access control.
AI models often interact with vector databases, embeddings (numerical representations of meaning used to compare concepts of high-dimensional space), and unstructured data, making it difficult to enforce traditional column or field-level access control. AI AWARE access helps regulate the content used during inference and ensures proper separation between models, datasets, and users.
4. Monitoring and drift detection.
AI is dynamic. Learn, adapt, and sometimes drift. Organizations need monitoring capabilities to track changes in inference patterns, detect behavioral abnormalities, and record complete input and output exchanges for forensics and compliance. For Agent AI, it includes decision path tracking and mapping activities across multiple systems.
5. Automating policy enforcement and response.
Real-time protection, which acts like an “AI Firewall,” can intercept prompts or output that violates content policies, such as malware generation or leaking sensitive information. An automated response mechanism can quarantine a model, revoke it, or roll back deployments within milliseconds.
A framework that guides implementation
Luckily, security teams don’t have to start from scratch. Some frameworks provide a solid blueprint for building security into AI workflows.
Integrating these frameworks with MLSecops practices helps organizations ensure the right layers at the right time, using the right controls. Start by enabling security teams to visualize the AI development pipeline. Building a bridge between data science and engineering peers. Invest in training staff on new threats and specialized tools. AI sequels are not just a tool challenge, they are strategic change. As AI systems evolve, an approach to risk, accountability and visibility is also needed. A true priority not only protects infrastructure, but also enables secure innovation at scale. Chief Information Security Officer Diana Kelley writes a column protecting the AISC media perspective by a trusted community of SC Media Cybersecurity subject matter experts. Each contribution has the goal of bringing a unique voice to key cybersecurity topics. We strive to ensure that our content is of the highest quality, objective and non-commercial.
