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AI Security Starts Here: Revisiting the Fundamentals

The security of AI systems often seems shrouded in novelty. But is it really? This three-part series explores AI security by separating what remains essential from what’s truly new. In Part 1, we’ll revisit foundational security paradigms. In Part 2, we’ll tackle the novel risks introduced by AI. Finally, Part 3 examines defenses uniquely suited to these threats.

Familiar Risks In Disguise

Generative AI security advice often feels revolutionary—but is it really?

Articles like “5 Serious Gen AI Security Mistakes to Avoid” highlight risks such as weak governance and excessive access, yet these are age-old security principles repackaged in AI-specific language.

This raises a key question: How much of AI security is truly new, and how much reflects established principles reinterpreted for modern challenges?

Let’s unpack this by exploring how traditional paradigms form the backbone of AI security.

1. Weak Governance

What the Article Highlights: Poor oversight leads to inconsistent practices and unmanaged risks in generative AI systems.

Traditional Principle: Governance frameworks like ISO 27001 and NIST CSF emphasize the importance of policies, accountability, and structured oversight.

Mapping: The same governance principles apply to AI: define clear roles for accessing models, enforce data lifecycle policies, and monitor compliance.

Takeaway: Weak governance isn’t unique to AI—it’s a persistent challenge across all systems lacking structured oversight.

• CISO Insight: Focus on embedding AI oversight into your existing security governance frameworks to avoid duplicating efforts.

• Security Engineer Insight: Ensure documentation and logging are standardized for AI-specific processes like model lifecycle management.

Governance failures often lead directly to downstream risks, such as data mismanagement—our next focus.

2. Bad Data Practices

What the Article Highlights: Poor data quality and governance increase risks, such as unreliable AI outputs or vulnerability to poisoning attacks.

Traditional Principle: Data lifecycle management and integrity are long-standing security practices ensuring data is validated, accurate, and reliable throughout its use.

Mapping: For AI systems, these principles apply to verifying the quality of training data, maintaining provenance, and ensuring proper sanitization before ingestion.

Takeaway: While AI increases the scale and consequences of data-related risks, these challenges stem from failures to uphold established principles of secure data management.

• CISO Insight: Establish enterprise-wide data validation and integrity checks as a baseline, ensuring AI aligns with broader data security goals.

• Security Engineer Insight: Implement tools to automate data sanitization and integrity validation during the AI training process.

Without proper access management, even validated data and well-defined governance structures can fail to protect AI systems effectively.

3. Excessive Access

What the Article Highlights: Over-permissive access to AI models, data, or APIs creates unnecessary vulnerabilities.

Traditional Principle: Least privilege access and role-based access control (RBAC) prevent unauthorized access and limit exposure.

Mapping: AI systems face the same risks when RBAC, access reviews, and API authentication are neglected.

Takeaway: Managing access is a traditional problem exacerbated by AI’s complexity, not an entirely new risk.

• CISO Insight: Conduct periodic reviews of AI-related access privileges, aligning with your organization’s least-privilege strategy.

• Security Engineer Insight: Secure API endpoints with token-based authentication and enforce granular access policies for model use.

Excessive access often ties into vulnerabilities inherited from third-party systems—let’s explore next.

4. Inherited Vulnerabilities

What the Article Highlights: Pre-trained models and third-party libraries introduce vulnerabilities into AI systems.

Traditional Principle: Supply chain security addresses risks from external software and dependencies.

Mapping: AI systems extend this principle by requiring validation of third-party models and ongoing monitoring for tampering or outdated components.

Takeaway: This is classic supply chain security, adapted to the AI context.

• For CISOs: Expand your supply chain risk assessments to include AI models, ensuring the organization vets third-party models rigorously.

• For Security Engineers: Automate validation of models before deployment and integrate supply chain monitoring into CI/CD pipelines.

5. Overlooking Internal Risks

What the Article Highlights: Focusing on external threats ignores insider misuse of AI systems and data.

Traditional Principle: Insider threat mitigation includes logging, anomaly detection, and privilege restrictions.

Mapping: AI-specific insider risks, like unauthorized fine-tuning or model misuse, fall squarely under existing practices for managing privileged access and monitoring activity.

Takeaway: Internal risks are not new but must be adapted to AI-specific workflows.

• For CISOs: Develop tailored insider threat programs addressing risks unique to AI workflows (e.g., API misuse or data leakage).

• For Security Engineers: Enable activity monitoring across training pipelines, APIs, and inference endpoints.

Laying the Groundwork for AI-Specific Challenges

After analyzing the “mistakes” highlighted in the Google article, a clear takeaway emerges: many risks labeled as “AI-specific” are better understood as lapses in applying foundational security principles. Governance, data integrity, access control, supply chain security, and insider threat management remain critical for protecting any system, AI included. These principles are not outdated—they form the bedrock of effective AI security.

The ultimate goal of security—whether for traditional systems or AI—is to maintain confidentiality, integrity, and availability (CIA). These principles remain universal and unchanging, even in the face of evolving technology. Foundational security practices like governance, access control, and data lifecycle management are critical to upholding CIA for AI systems.

That said, foundational principles are not the entire solution. AI introduces complexities that traditional frameworks were never designed to address. For instance, the lines between control and data planes blur in AI systems, especially in generative AI like large language models, where user inputs can influence model behavior. Adversarial attacks, data poisoning, and model-specific vulnerabilities demand new approaches tailored to these emerging threats.

In the next part of this series, we’ll build on these foundational concepts and examine how AI-specific risks challenge existing security paradigms. We’ll explore the novel attack vectors and vulnerabilities introduced by modern AI systems, setting the stage for solutions uniquely suited to these threats. Stay tuned for Part 2: What’s New – Unique Challenges and Novel Attack Vectors in AI Security.

References

1. Google Cloud: 5 Serious Gen AI Security Mistakes to Avoid

2. RSA Conference: The Importance of Governance in AI Security

3. arXiv: Understanding Data Integrity Risks in AI

4. USENIX: Challenges of AI Supply Chain Security

5. Dark Reading: Addressing Insider Threats in AI Systems

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All views and opinions expressed therein are solely the authors’ own and do not reflect those of any employers past or present.

NoFluffSec is a Bitsavant LLC publication