If you’re deploying AI systems and relying on traditional security controls, you have blind spots. AI introduces attack surfaces that most security teams aren’t equipped to monitor, and threats that standard detection tools won’t catch.

This isn’t fear-mongering. It’s the reality of a technology that processes data in fundamentally different ways than traditional software.

AI-Specific Threats Are Real

Traditional cybersecurity focuses on protecting systems and data from unauthorized access. AI security adds new dimensions:

Data Poisoning: Attackers can manipulate the data used to train AI models, causing them to produce incorrect or biased outputs. This can happen gradually, making it difficult to detect until significant damage is done.

Model Theft: Your trained models represent significant intellectual property and competitive advantage. Unlike source code, models can sometimes be extracted through careful querying of APIs.

Adversarial Attacks: Carefully crafted inputs can cause AI systems to misclassify or malfunction in ways that are invisible to human observers. A slight modification to an image, undetectable to humans, can completely change how an AI system interprets it.

Prompt Injection: For systems using large language models, malicious inputs can manipulate system behavior in unintended ways, potentially exposing sensitive data or executing unauthorized actions.

Why Traditional Security Falls Short

Standard security tools and approaches weren’t designed for AI systems:

Perimeter security doesn’t help when the threat comes through legitimate API calls that slowly extract model capabilities.

Signature-based detection can’t identify adversarial inputs that look normal to traditional analysis.

Log analysis may not capture the subtle patterns that indicate model manipulation or data poisoning.

Incident response playbooks don’t account for AI-specific attack vectors or remediation steps.

This doesn’t mean traditional security is irrelevant. It means AI systems require additional, specialized protections.

Building AI-Aware Security

Effective AI security integrates traditional cybersecurity fundamentals with AI-specific controls:

Model Monitoring

Continuously monitor model behavior for drift, anomalies, or unexpected outputs. Establish baselines during development and alert on deviations that could indicate manipulation.

Input Validation

Implement robust validation for all data entering AI systems. This includes both training data and runtime inputs. Look for patterns that could indicate adversarial manipulation.

Access Control

Apply least-privilege principles to AI systems. Limit who can query models, access training data, or modify system configurations. Monitor and log all access.

Supply Chain Security

AI systems often depend on pre-trained models, libraries, and datasets from external sources. Validate the integrity of these components and monitor for compromises.

Incident Response

Develop AI-specific incident response procedures. When a model is compromised, what’s the remediation path? How do you validate that the threat has been contained?

The Role of MDR for AI Systems

Managed Detection and Response (MDR) provides 24/7 monitoring and expert response capabilities. For AI systems, effective MDR requires:

  • AI-aware threat intelligence: Understanding the evolving landscape of AI-specific attacks
  • Specialized detection rules: Identifying patterns that indicate model manipulation, data poisoning, or adversarial attacks
  • Expert analysts: Security professionals who understand both traditional threats and AI-specific risks
  • Rapid response: Capabilities to contain and remediate AI security incidents

Generic MDR services may not have these capabilities. Ask specific questions about AI security expertise when evaluating providers.

Integrating Security Into AI Development

Security shouldn’t be an afterthought. Build it into your AI development process:

Design phase: Threat model your AI application. What are the potential attack vectors? What data is at risk?

Development phase: Implement security controls as part of the development process. Test for adversarial robustness.

Deployment phase: Configure monitoring, establish baselines, and integrate with your security operations.

Operations phase: Continuously monitor, update threat models, and adapt to new attack techniques.

What’s Next?

AI security is an evolving field. The threats we see today will be different from the threats we face in two years. Organizations need security approaches that can adapt and evolve.

If you’re deploying AI systems and want to understand your security posture, we can help. Our security assessments identify vulnerabilities specific to AI implementations and provide actionable recommendations for remediation.

Want to assess your AI security posture? Schedule a discovery call to discuss your specific environment and concerns.