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AI Governance & Compliance: Securing AI Systems with New AI-Specific Security Tools

  • Writer: Skeet Spillane
    Skeet Spillane
  • Sep 19
  • 3 min read
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AI is moving fast—and most security teams are being asked to secure it with tools and frameworks that weren’t designed for the job.


At Pillar, we’re seeing growing concern from CISOs about how to handle threats like data poisoning, prompt injection, and model misuse. These aren’t theoretical risks—they’re operational ones, and they’re already showing up in enterprise environments.


In this piece, I’ve laid out how AI security differs from traditional cybersecurity, what capabilities matter most, and why we need to rethink how we secure the full AI lifecycle.



AI Security: A New Attack Surface, A New Defense Stack


AI is the fastest-growing attack surface of this decade. The same innovations transforming your business are also introducing novel threats—data poisoning, adversarial manipulation, model leakage, and API exposure—that traditional security models weren’t built to handle.


While conventional cybersecurity focuses on protecting networks, endpoints, and data, AI security demands a new layer of defense. We're talking about securing training data pipelines, model integrity, prompt engineering, and the automated decisions these systems are making on our behalf.


At Pillar, we see this not as a challenge to fear, but as the next evolution of the security stack.


What Makes AI Security Different?


Expanded Attack Surfaces: It's no longer just endpoints and users. Now, attack vectors include training datasets, prompts, model APIs, and even the logic of the models themselves.

New Threats: Data poisoning, adversarial inputs, prompt injection, and model exfiltration are just the beginning.

Governance Gaps: Frameworks like SOC 2, ISO 27001, and HIPAA weren’t designed for self-learning, autonomous systems. We need AI-specific governance.

AI systems don’t just process data—they ingest and learn from it. One poisoned dataset or manipulated prompt can cascade into business-critical failures.


AI Security vs. Traditional Cybersecurity

  • Data Integrity vs. Network Perimeter: AI security must ensure datasets and training pipelines aren’t corrupted.

  • Model Robustness vs. Malware Defense: Instead of scanning files, AI security stress-tests models against adversarial attacks.

  • Explainability vs. Monitoring: Traditional tools log events. AI security demands explainability and auditability to ensure ethical, accurate, and compliant outputs.


The AI Security Toolbox: Categories & Capabilities

Securing AI requires layered defenses that blend traditional practices with new AI-specific capabilities. Here's where we're focusing. Keep in mind…Some of these tools are brand new in a new space and the market is quickly evolving.


  • Cloud & Infrastructure Security: AI workloads are largely cloud-based and inherit all the CSPM and workload security challenges.

    • Examples: Wiz, Orca, Prisma Cloud, AzureAD (Entra), GCP IAM

  • Data Provenance & Integrity: Tools that verify dataset origins, monitor for drift, and prevent data poisoning.

    • Examples: Mostly AI, Snorkel AI, DataRobot, EvidentlyAI, MLFLow, Arize

  • Model Security & Robustness: Solutions that test models for adversarial resilience and monitor their behavior in production.

    • Examples: Robust Intelligence, HiddenLayer, Lakera, GuardrailsAI, AutoGen

  • AI Governance & Compliance: Platforms enforcing responsible AI policies, audit trails, and regulatory alignment.

    • Examples: Credo AI, CalypsoAI, Monitaur, AICert, MLFlow

  • Identity, Access & Secrets Management: Protecting model access, prompt injection risks, and API key leakage.

    • Examples: HashiCorp Vault, CyberArk, Okta, AzureAD (Entra) GCP IAM

  • Monitoring & Threat Detection for AI Workloads: Cloud-native security evolving to cover AI pipelines.

    • Examples: Microsoft Defender for Cloud, Prisma Cloud, SentinelOne Purple AI, Grafana, EvidentlyAI, Arize


What’s Next: Building the AI Security Framework


We’re actively developing a practical AI Security Framework that helps mid-market security teams operationalize these capabilities. One that aligns with existing controls, but brings clarity to the new risks introduced by AI.


Our belief is simple: You can’t bolt on AI security after the fact. You have to build it into the lifecycle—from ingestion and training to deployment and continuous monitoring.


As Ferris Bueller said, "Life moves pretty fast. If you don't stop and look around once in a while, you could miss it."


AI is moving fast. So are the attackers. Let’s not miss this moment to get ahead.



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