By Eric Lansbery – COO

Introduction: A Brief History of AI and Its Cybersecurity Impact 

Artificial Intelligence (AI) has evolved from theoretical concepts in the 1950s to transformative technologies embedded in every facet of modern enterprise. From Alan Turing’s foundational work to the rise of generative AI, the journey has been marked by breakthroughs in machine learning, deep learning, and natural language processing. Today, AI is both a powerful defense mechanism and a potential attack vector. Cybersecurity teams now face adversaries equipped with AI-driven tools capable of launching sophisticated phishing campaigns, automating reconnaissance, and exploiting vulnerabilities at scale. In response, organizations are integrating AI into their security operations to detect anomalies, reduce alert fatigue, and accelerate incident response. 

Summary 

This article explores the evolution of artificial intelligence (AI) in cybersecurity, emphasizing both its defensive advantages and the new risks it introduces. It highlights current trends such as the use of AI in security operations to detect threats and streamline incident response, as well as the growing importance of governance and compliance as organizations deploy AI in cloud environments.  

The article also outlines key frameworks for AI security strategy, including ISO/IEC 42001, the OWASP AI Security Guide, and the NIST AI RMF, and underscores the need for CISOs to approach AI as a strategic asset, supported by robust governance and continuous monitoring. 

Current AI Trends Reshaping Cybersecurity 

Several key trends are driving the convergence of AI and cybersecurity: 

• AI-Powered Threat Detection 

• Identity and Access Management (IAM) 

• Generative AI Risks 

• AI in Security Operations Centers (SOCs) 

• AI Governance, Policy, and Design Principles 

• Third Party Risk Management 

These trends are accelerating the integration of advanced analytics and automation into enterprise security postures, enabling organizations to proactively identify and address threats before they escalate.

As AI-driven tools become more sophisticated, they are increasingly adept at correlating disparate signals, automating identity verification, and adapting to evolving attack techniques within Security Operations Centers.

However, the rapid adoption of generative AI also introduces novel risks, such as data poisoning and model manipulation, requiring security teams to implement adaptive controls and continuously evaluate their risk landscape. In this environment, the intersection of AI and cybersecurity is not only redefining traditional defense mechanisms but also reshaping the governance strategies that underpin secure cloud deployments, setting the stage for a new era of collaborative risk management and shared responsibility. 

AI Governance and Security in Cloud Platforms 

As enterprises deploy AI workloads in the cloud, governance and security become paramount. Organizations must implement robust controls that address both the unique challenges of AI—such as model integrity, data provenance, and algorithmic transparency—and the broader concerns of cloud security, including access management and regulatory compliance. This requires continuous monitoring of AI models for signs of drift or malicious manipulation, coupled with advanced identity and access policies that limit exposure to sensitive data and critical systems. By leveraging real-time analytics and automation, security teams can proactively respond to threats targeting AI infrastructure, ensuring that cloud-native deployments remain resilient and trustworthy.

The integration of these measures not only strengthens defense against emergent risks but also fosters a culture of responsible AI use, positioning enterprises to adapt rapidly as threat landscapes evolve in various cloud hosting platforms and solutions. 

• AWS emphasizes responsible AI through its Cloud Adoption Framework, including governance policies, risk management tools, and cost optimization strategies. 
• Microsoft Azure integrates AI governance using the NIST AI RMF, focusing on responsible AI principles, risk assessment tools, and policy enforcement. 
• Google Cloud’s Vertex AI platform offers built-in data governance, security enhancements, and a shared fate model for collaborative responsibility. 

Frameworks for AI Security Strategy 

To build a robust AI security strategy, CISOs should align with established frameworks within existing or newly established Information Security Management Systems (ISMS). AI governance in the cloud presents additional challenges in managing threats and vulnerabilities. Some of the emerging frameworks and program extensions for AI have been further developed in recent years: 

• ISO/IEC 42001:2023: Structured approach to responsible AI development. 

• OWASP AI Security Guide: Threat modeling and controls for AI systems. 

• NIST AI RMF: Map, Measure, Manage, and Govern AI risks. 

• Risk Management Framework (RMF): Lifecycle risk management for AI systems. 
• Cloud Security Alliance (CSA) AI Controls Matrix

In addition to adopting ISO/IEC 42001:2023 for a responsible AI development framework, CISOs should consider integrating complementary guidelines such as the OWASP AI Security Guide, which provides actionable controls for addressing vulnerabilities specific to AI systems, and the NIST AI Risk Management Framework (RMF), which emphasizes risk identification, governance, and ongoing evaluation. By layering these frameworks, organizations can systematically address both technical and organizational risks, ensure transparency in algorithmic decision-making, and enable continuous improvement in AI security posture. This multifaceted approach empowers security leaders to foster resilience and accountability in environments where cloud-native AI solutions are rapidly evolving, laying the groundwork for a comprehensive defense-in-depth strategy that keeps pace with emerging threats and regulatory standards. 

It’s important to note that none of these guidelines and standards by themselves fully manage AI security risks or controls. Instead, organizations must take a layered, holistic approach—combining multiple frameworks and best practices to cover the breadth of technical, operational, and regulatory requirements inherent to cloud-based AI deployments. By integrating complementary standards and continuously evolving their security posture, enterprises can address gaps, mitigate emerging threats, and ensure that AI strategies remain robust and adaptable as both technology and risk landscapes shift.  

This comprehensive methodology enables security teams to move beyond compliance, fostering a proactive stance that supports innovation while safeguarding organizational interests. 

AI Third Party Risk Management 

AI Third Party Risk Management is an essential component of a resilient security strategy, especially as organizations increasingly rely on external vendors for AI solutions and cloud infrastructure- often times with AI already built into the solutions.  

CISOs must establish rigorous due diligence processes to assess third-party providers, ensuring their practices align with internal security frameworks and regulatory expectations.  

This involves evaluating vendor transparency, contract terms addressing AI data governance, and continuously monitoring shifts in their risk posture.  

Existing tools are already utilizing AI risk management and AI-based security operations tasks, such as: 

• Endpoint Protection: The built-in services provide security for multi-cloud and hybrid environments as part of common productivity stacks, such as Microsoft Defender. They are already integrated with generative AI solutions like Security Copilot for incident investigation, threat hunting, and automated response. 

• Agentless Cloud Posture Management: These solutions provide full-stack visibility and risk assessment across multi-cloud environments, and use AI to correlate risks across identities, workloads, and data to prioritize remediation. They also integrate with CI/CD and DevOps. 

• Zero Trust Architecture Solutions: Unified protection solutions that span network, cloud, endpoints, and users that consolidate security management points for zero trust are increasingly powered by AI- making decisions on identity management and trust verification along the way. 

• Runtime Security Monitoring Solutions: Threat detection and automated response in containerized cloud workloads challenge the traditional way of securing application runtime. Today’s third-party solutions integrate directly with DevOps pipelines and use AI to produce real-time results. 

By integrating robust third-party oversight into the overall AI governance strategy, organizations can mitigate supply chain vulnerabilities and maintain compliance across increasingly complex digital ecosystems, thereby reinforcing trust and operational integrity as they advance toward a secure and agile AI future. 

Conclusion: Building a Secure AI Strategy 

Leaders in Security, Legal, Human Capital Management, and Information Technology alike must treat AI not just as a tool, but as a strategic asset requiring governance, protection, and continuous monitoring. By leveraging cloud-native security capabilities and aligning with global frameworks, organizations can mitigate AI-specific risks, ensure compliance, and build trust. 

Looking ahead, organizations that embed AI governance into their broader security culture will be best positioned to adapt as regulatory requirements and threat vectors evolve. Proactive engagement with cross-functional stakeholders, ongoing workforce training, and investment in explainable AI technologies will further empower security teams to anticipate challenges and uphold organizational integrity. By prioritizing transparency, accountability, and agility, CISOs can ensure that AI initiatives drive innovation while upholding the highest standards of privacy and security, ultimately enhancing the enterprise’s competitive edge in an increasingly digital world. 

Ready to Tackle Your AI Security in the Cloud Concerns and Challenges? 

Organizations that are starting to adopt Artificial Intelligence (AI) into their technology and operations don’t just implement without protecting their business data—they create a stronger, more adaptable security program. By taking a proactive approach, you ensure that AI model security management isn’t just written, but lived—guiding everyday security decisions, reducing risk, and enabling growth. 

Seiso specializes in helping organizations build, refine, and implement effective information security management systems that align with compliance, reduce risk, and support business goals. Whether you need to establish an AI security practice from the ground up, update outdated policies to adopt AI, or improve governance and enforcement, our experts can guide you every step of the way. 

Let’s make your AI risk management a differentiator, not a liability. Get in touch with Seiso to discuss how we can help you create a clear, enforceable, and scalable policy framework that protects your business and accelerates growth.

Get in touch to extend your security practices into AI security wins.