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ServiceNow's Neeraj Jain emphasized that embedding AI governance throughout the lifecycle—from intake to deployment—enables enterprises to scale artificial intelligence initiatives more rapidly and securely. He argued that effective governance is not a bureaucratic hurdle but a strategic accelerator, reducing technical debt and misconfigurations while providing real-time access to authoritative data sources. Jain highlighted the importance of visibility into all AI assets to mitigate risks such as reputational damage, legal fines, and compliance failures, and described how ServiceNow's AI Control Tower offers comprehensive oversight for organizations deploying AI agents.
In contrast, the travel industry is focusing on the management of non-human identities (NHIs), such as automated systems and applications, to address evolving cybersecurity threats. Effective NHI and secrets management is becoming critical across sectors, with integrated systems providing lifecycle oversight, ownership tracking, and vulnerability management. While both discussions address AI-related security, the first centers on governance frameworks for enterprise AI deployment, and the second on operational security for machine identities, particularly in the travel sector.
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In an interview published on 2025-12-15, ServiceNow's Neeraj Jain said organizations should embed AI governance from intake through deployment so they can scale AI initiatives faster while reducing security, legal, and compliance risks. He highlighted structured intake, visibility into the AI stack, and early involvement of security, legal, and compliance teams as key elements of this approach.
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Enterprises are facing a critical inflection point as artificial intelligence becomes deeply embedded across organizational layers, fundamentally altering cyber risk and security postures. Research from industry leaders and the Cloud Security Alliance highlights that mature governance frameworks are now the primary differentiator for organizations confident in their ability to secure AI systems. As AI agents and machine identities proliferate, traditional identity and access management models are proving inadequate, with identity emerging as the new control plane for managing AI risk. The rapid adoption of AI, often without sufficient oversight, is creating new blind spots, expanding attack surfaces, and introducing risks such as shadow AI, where unsanctioned tools and agents operate outside established security controls. Security teams are increasingly involved in AI adoption, leveraging AI for detection, investigation, and response, but the lack of comprehensive governance and workforce training remains a significant barrier. The convergence of AI with other technologies, such as blockchain and cryptocurrency, is also driving the emergence of autonomous financial systems and agentic payments, further complicating the security landscape. Success in this new paradigm requires balancing innovation with robust accountability, ensuring that AI-driven systems are auditable and governed rather than left to unconstrained automation. As organizations move from experimentation to operational deployment of AI, the need for continuous, data-aware identity security and formal governance policies is paramount to mitigate risks, ensure compliance, and maintain confidence in AI-enabled operations.
Jun 29, 2026
Boards of directors are increasingly prioritizing cybersecurity, with a particular focus on how artificial intelligence (AI) and automation are transforming both risk and defense strategies. As AI systems evolve from predictive models to agentic systems capable of autonomous action, organizations are seeing significant improvements in threat identification and response times. Reports indicate that companies deploying agentic AI for security use cases experience substantial returns on investment, with notable gains in operational efficiency and risk reduction. This shift is prompting boards to formalize AI oversight, integrate data privacy and security into all deployments, and ensure that early successes with AI can be scaled across the enterprise. At the same time, the threat landscape is intensifying as cybercriminals weaponize AI to bypass traditional defenses, making responsible AI governance essential for building resilient security architectures. Experts emphasize that effective AI governance frameworks not only enhance an organization’s ability to predict and counter sophisticated attacks but also help align cybersecurity initiatives with broader business objectives. The dual role of AI—as both a tool for defenders and attackers—underscores the need for leadership, accountability, and robust governance to ensure that AI-driven security measures deliver measurable business value while mitigating emerging risks.
Jul 12, 2026
Enterprises are rapidly integrating artificial intelligence into their risk management and operational processes, but governance and security controls are struggling to keep pace. According to AuditBoard, more than half of organizations have implemented AI-specific tools, and many are investing in machine learning training for their teams. Despite this widespread adoption, confidence in AI systems remains uneven, with few organizations feeling prepared for the governance requirements that new AI regulations will demand. The pace of AI experimentation surged in May and June 2025, only to decline in July as acceptance rates dropped and decision times increased, highlighting volatility in adoption and a lack of robust governance structures. Many organizations find themselves in a 'middle maturity trap,' where initial enthusiasm for AI and risk frameworks fades without sustained governance and oversight. Boards that prioritize risk oversight as a regular agenda item and align on shared performance goals see more consistent progress, while others experience stagnation and last-minute compliance efforts. Control maturity is closely tied to governance, with rapid adoption of controls in some periods followed by slowdowns and only partial recoveries. As regulatory expectations expand to cover AI, cybersecurity, and environmental reporting, the ability to embed controls into daily operations will be critical for resilience. Simultaneously, the rise of autonomous AI agents with significant system privileges introduces new identity and access management challenges. These agents can execute code, handle sensitive data, and perform complex tasks without human intervention, increasing the risk of automation errors leading to major incidents. The traditional security perimeter has shifted, making identity management the central control point for modern enterprises. The 2025-2026 SailPoint Horizons of Identity Security report reveals that fewer than 40% of AI agents are governed by identity security policies, leaving a substantial gap in enterprise security frameworks. The proliferation of non-human identities and automated systems has dramatically expanded the attack surface, making organizations without comprehensive identity visibility especially vulnerable. Mature identity security practices are now seen as a strategic necessity, not just a compliance checkbox. Organizations are mapping controls to multiple frameworks, but the depth of implementation varies widely, with leading firms embedding thousands of requirements into daily operations. The convergence of rapid AI adoption, evolving risk frameworks, and the need for robust identity governance underscores the urgent need for enterprises to strengthen their risk and security postures. Without clear governance structures and comprehensive identity management, the benefits of AI could be undermined by increased exposure to operational and security risks. Boards and CISOs must ensure that risk oversight, control adoption, and identity security are integrated into the core of enterprise strategy to navigate the evolving threat landscape effectively.
Jun 29, 2026