Executive Summary
Artificial intelligence (AI) is transforming industries, driving automation, improving decision-making, and enabling new efficiencies. However, its rapid adoption introduces unique security risks that traditional cybersecurity measures are not equipped to handle. AI models are vulnerable to data poisoning, adversarial inputs, model theft, and prompt-injection attacks, making them prime targets for exploitation. Adding to these challenges is the rise of agentic AI—autonomous AI agents capable of making decisions and executing tasks with minimal human intervention. These systems introduce new risks, such as unchecked autonomy, adversarial manipulation, and unintended consequences, further amplifying the need for a proactive AI security strategy.
This report provides security and technology leaders—including CIOs, CISOs, CDOs, and other key stakeholders—with a comprehensive analysis of AI and agentic AI security risks, regulatory challenges, and best practices for mitigation. It explores how AI-specific vulnerabilities differ from traditional cybersecurity risks and why securing AI systems requires new approaches. With agentic AI increasingly embedded in automated workflows, decision-making systems, and cybersecurity operations, organizations must rethink their security frameworks to ensure that these AI-driven agents remain controllable, auditable, and aligned with organizational objectives. The report also examines the evolving regulatory landscape, including guidance from NIST AI RMF, the EU AI Act, and ISO/IEC 42001, while outlining a strategy for aligning AI security efforts with compliance requirements.
AI security is not just a technical challenge but also a strategic imperative requiring executive buy-in and cross-functional collaboration. Data governance is foundational, because securing AI begins with ensuring the integrity and provenance of training data and model inputs. Security teams must develop new expertise to handle AI-driven risks, and business leaders must recognize the implications of autonomous AI systems and the governance frameworks needed to manage them responsibly. Industries such as healthcare and finance, where AI decisions have real-world consequences, face even greater scrutiny, necessitating proactive security measures before regulations mandate compliance.
As AI adoption accelerates and agentic AI systems gain more autonomy, organizations must shift from reactive security approaches to proactive risk management. This report serves as a strategic guide for CxOs and security leaders, equipping them with the knowledge to assess AI risks; implement security best practices; and build resilient AI systems that maintain trust, compliance, and operational security in the world of increasingly autonomous AI.
