AI Security is Greater than Model Testing, It's an AppSec Problem
Learn why effective AI red teaming must go beyond model attacks to focus on securing the entire application against real-world threats.
Fergal Glynn
AI systems are increasingly embedded in critical business workflows, infrastructure, and decision-making. But traditional security postures, designed around static infrastructure and human-written code, fail to account for the dynamic, data-driven nature of AI pipelines.
AI-SPM is the discipline of continuously identifying, assessing, and hardening the security posture of AI systems across their full lifecycle, from data ingestion to model deployment.
In this guide, you’ll learn what AI-SPM is and how it works, as well as the benefits of implementing AI-SPM in your organization.
AI Security Posture Management is a continuous process that spans the full lifecycle of your AI stack. From mapping assets to automating remediation, AI-SPM gives security teams the tools to identify risks early, enforce policies consistently, and maintain visibility across dynamic environments. Here’s how it works.
The first step is visibility. AI-SPM tools scan your environment to map out all AI assets: models, training pipelines, datasets, vector stores, APIs, third-party integrations, and inference endpoints. This includes identifying shadow models and unauthorized deployments often overlooked by IT.
AI-SPM tools fingerprint and track every asset, collecting metadata such as model architecture, training parameters, input/output types, and training data sources to help contextualize downstream risks.
AI systems introduce novel attack surfaces. AI-SPM systems map the attack surface of each model and component, down to the vector indexes, embeddings and model interfaces, evaluating each AI asset’s exposure to threats like:
This analysis also covers inherited risk, such as a fine-tuned model that sits on top of a compromised foundation or pulls features from a tainted dataset.
To stop configuration creep, unauthorized access, and quiet model degradation before they become threats, AI-SPM enforces configurable security policies, defining trusted data sources, approved models for production, who can invoke inference, and required logging standards.
But models change. AI-SPM tracks configuration drift, policy violations, and unauthorized changes to production systems. For example:
A core component of AI-SPM is focusing security resources where the business has the most at stake. To do so, each asset is assigned a dynamic risk score based on:
High-risk assets can be automatically flagged for review or remediation. Risk scoring can also be aligned with regulatory requirements, such as ISO 42001 and NIST AI RMF.
AI-SPM integrates with existing DevSecOps pipelines to automate remediation and enforce guardrails, making AI security programmable, auditable, and scalable. It enables actions such as:
AI security isn’t a set-and-forget operation. Posture must be monitored continuously. AI-SPM tools provide:
This is essential for organizations deploying GenAI at scale or under regulatory scrutiny, helping to maintain real-time assurance and provable governance over your AI stack.
AI systems are quickly becoming the highest-value targets—and often high-risk assets. As more enterprises deploy large language models (LLMs), autonomous agents, and other AI pipelines in production, the attack surface is exploding. AI-SPM is designed to monitor, assess, and harden the security posture of these AI workloads in real time.
Here’s a look at the benefits of AI-SPM.
AI-SPM continuously scans AI infrastructure for misconfigurations, exposed endpoints, overprivileged service accounts, and drift in deployed models. It identifies problems before attackers find them.
AI-SPM aligns AI deployments with evolving frameworks like NIST AI RMF and industry mandates (ISO/IEC 42001, GDPR, HIPAA). It automates the detection of policy violations in training data usage, model access, and auditability.
AI-SPM collects telemetry from training pipelines, inference endpoints, and orchestration layers across hybrid environments. Security teams gain full insight into where AI models reside, how they’re accessed, and what data they operate on.
AI-SPM learns how your models are used in the real world, then flags anomalous behavior and recommends guardrails. This is especially important for organizations deploying generative models or autonomous agents in regulated or sensitive use cases.
By automating the monitoring and remediation of AI-related risks, AI-SPM eliminates alert fatigue and accelerates response times. Security engineers can focus on proactive defense and offensive security rather than reactive incident response.
AI-SPM is essential for any organization deploying AI systems. It reduces risk exposure, streamlines compliance, and maximizes the efficiency of your existing team. Instead of reacting to threats after damage is done, AI-SPM gives you the tools to catch them at the source: misconfigurations, overpermissive access, and unsecured model endpoints.
From shadow AI deployments to fragmented cloud infrastructure, AI-SPM restores visibility and control across your entire AI ecosystem. The result: tighter configurations, faster remediation, and stronger defenses against targeted attacks on AI systems.
But even the best tools need sharp eyes behind them. Mindgard’s Offensive Security for AI stress-tests your models against real-world threats (prompt injection, model inversion, data leakage, and more) before attackers get the chance. See the Mindgard difference firsthand: Book your demo now.
AI-SPM is specifically designed to address the dynamic, data-driven nature of AI systems, whereas traditional security posture management focuses on static infrastructure and human-written code. AI-SPM continuously monitors AI-specific risks (e.g., prompt injection, model inversion, data poisoning) and enforces policies tailored to AI workflows.
Traditional cybersecurity tools (like firewalls and endpoint protection) are not equipped to handle AI-specific threats such as adversarial attacks, model drift, or training data poisoning. AI-SPM provides specialized monitoring, risk scoring, and remediation for AI systems, ensuring comprehensive protection.
Yes, AI-SPM works alongside CI/CD pipelines, SIEMs, and cloud security tools to automate risk detection, policy enforcement, and remediation (e.g., blocking unsafe model deployments or isolating compromised datasets).
Yes, AI-SPM identifies threats like prompt injection (for LLMs), model evasion attacks, and data poisoning by analyzing model behavior, input patterns, and output anomalies. It can trigger automated guardrails (e.g., blocking malicious prompts or retraining compromised models).
