By Brian DiPietro, Cyber Executive, CISO, Advisor
As AI continues to transform enterprise operations, Chief Information Security Officers (CISOs) must stay ahead of emerging threats while enabling innovation. AI’s integration into business workflows, security programs, and decision-making brings unparalleled opportunities but also new, complex risks. The security landscape is shifting, and organizations must rethink their strategies to protect their AI investments. However, CISOs play a critical role in not only mitigating these risks but also ensuring AI applications are secure, compliant, and ready for deployment.
AI: A Force for Both Defense and Attack
AI is playing an increasing role in security operations, from automated threat detection to real-time incident response. Advanced machine learning models are enabling CISOs to identify threats faster, analyze vast datasets, and predict cyberattacks before they happen. But this same technology is being weaponized by attackers—malicious actors now use AI to generate convincing phishing emails, automate social engineering campaigns, and create adaptive malware.
The dual-use nature of AI forces security teams to ask: How do we leverage AI without exposing ourselves to new risks? A key part of the answer lies in understanding and securing AI at the inference layer—where AI models interact with real-world data, users, and enterprise systems.
The Inference Layer: The New Battleground for Security
For many enterprises, AI security conversations often focus on data security, compliance, and model integrity during training and development. But in reality, most security risks emerge after deployment, at the inference layer.
The inference layer is where AI models process inputs, generate outputs, and interact with external systems. Unlike traditional IT infrastructure, which has well-defined security controls, AI inference is dynamic and unpredictable. Attacks targeting inference can include:
- Prompt Injection & Manipulation – Threat actors craft inputs that cause AI models to generate harmful, misleading, or unauthorized responses.
- Data Leakage – AI applications can inadvertently expose proprietary, sensitive, or personally identifiable information.
- Model Exploitation – Attackers probe AI models for vulnerabilities, extracting underlying training data or modifying outputs for financial or strategic gain.
- Automated Adversarial Attacks – Malicious inputs designed to manipulate AI behavior, causing inaccurate, biased, or unsafe responses.
For CISOs, securing AI inference requires real-time monitoring, adaptive defenses, and governance frameworks that align with broader enterprise security policies. AI security isn’t just about defending against external threats—it’s about maintaining control over how models behave in real-world applications.
The Rise of Shadow AI: An Unseen Threat
Another challenge for CISOs is the rapid proliferation of Shadow AI—unsanctioned AI tools used by employees without security oversight. Just as Shadow IT posed risks in the cloud era, Shadow AI introduces new vulnerabilities, from compliance risks to unintended data exposure. Security teams must establish clear AI governance policies, ensure visibility into AI usage, and enforce access controls to prevent unauthorized deployment of AI models.
Regulatory Pressures Are Rising
Governments and regulatory bodies are increasingly focused on AI security, privacy, and accountability. The European Union’s AI Act, State-by-State legislation in the U.S. like the Utah Act, and other frameworks demand transparency in AI decision-making, risk management, and compliance. Enterprises need robust auditability, explainability, and red-teaming processes to meet evolving regulatory requirements.
Preparing for the Future: What CISOs Must Do Now
To stay ahead of AI security threats, CISOs should focus on:
- AI Security by Design: Integrate security from the ground up in AI projects. Ensure risk assessments are conducted before AI applications go live.
- Inference-Layer Security: Implement real-time monitoring, access controls, and adversarial testing to secure AI’s interactions with enterprise systems and users.
- Governance & Visibility: Develop company-wide policies that define approved AI models, usage guidelines, and compliance measures.
- Security Awareness & Training: Educate employees and leadership on AI risks, particularly around social engineering and adversarial attacks.
- Incident Response for AI: Adapt existing cybersecurity incident response plans to account for AI-specific attack scenarios.
Conclusion
AI is reshaping the security landscape, and CISOs must take a proactive, strategic approach to AI security. The key is balancing innovation with security, ensuring AI remains an enabler rather than a vulnerability. By focusing on inference-layer defenses, AI governance, and regulatory compliance, enterprises can harness AI’s full potential while mitigating emerging threats.
The future of AI security isn’t about stopping AI—it’s about securing it, ensuring that businesses can deploy it responsibly, safely, and at scale.
