Early AI security efforts focused on static defenses: think regex-based filters, simple prompt sanitization, and keyword lists. These are still useful for guarding against common vulnerabilities like prompt injection or PII leaks – but the attack surface has expanded. 

In modern AI deployments, especially those using agentic systems, models are no longer just responding to discrete queries. They’re chaining tasks, retaining context, and interacting with other systems. As a result, risk manifests not just in one-off outputs, but in emergent behavior over time.

This rapid transition from static prompts to dynamic systems means that filtering AI inputs and outputs is no longer enough. Security must be capable of inspecting and governing behavior across agents, APIs, MCP-based tooling, data sources, and model interactions.

Prebuilt scanners are essential to get started with AI security. They provide immediate coverage for a broad set of known threats, including PII exposure, code execution attempts, and jailbreak techniques. But they're trained on generalized datasets, not business-specific content.

They don’t account for domain-specific risks, organizational policy differences, geographic rules, or threat models based on internal knowledge. For example, what constitutes sensitive data varies significantly between a fintech company and a global media brand. Or consider regulatory frameworks like GDPR, HIPAA, and the EU AI Act, which impose obligations that static filters alone can’t enforce, especially when operating across multiple jurisdictions.

Generalized detection logic provides baseline hygiene to organizations. But enterprise-grade security requires enterprise-specific controls.

Custom scanners let organizations transform business policy into executable AI security logic. This bridges the gap between high-level governance frameworks and model-layer enforcement. 

Technically, custom scanners can:

This level of control enables precision in detection and the ability to tailor thresholds, coverage, and enforcement actions to align with actual business risk.

In live environments, change is constant—new threats emerge, policies shift, and different business units operate under different constraints. Rigid controls can’t keep up. Custom logic gives security teams the ability to respond in real time, tuning enforcement thresholds, adapting protections by geography or application, and integrating seamlessly with existing security infrastructure.

This flexibility is essential not just for stopping attacks, but for ensuring security keeps pace with deployment. When defenses can evolve alongside models and workflows—without introducing latency, false positives, or engineering delays—AI moves from experimental to operational, safely.

As organizations shift from using AI as a tool to embedding it across workflows, what was once considered a security feature must now be defined as architecture.

Custom scanners enable:

With custom scanners, security shifts from being a patch on top of models to a programmable layer that governs their use.

Custom scanning is also the foundation for the next phase of AI security: adaptive policy systems that can enforce guardrails dynamically and remediate issues in real-time.

For agentic systems in particular, this means:

To protect agentic AI, security systems require a feedback-driven, behavior-aware policy engine. Custom logic is the only way to get there.

Out-of-the-box protections cover the basics. But the strongest AI defense for generative and agentic systems requires understanding the difference between a dangerous output and an unacceptable action in your context, under your policies, and at your speed.

Custom security scanners give teams the tools to encode what matters; to define acceptable use, detect real risk, and respond with confidence. It shifts security from reactive detection to proactive governance.

Custom logic turns risk awareness into risk control. The outcome: secure innovation.