2 weeks Ago
Art Gilliland, CEO at Delinea . Leaders worldwide are promoting minimal AI regulation to drive innovation and avoid hindering growth. This strategy empowers businesses to explore new applications without excessive oversight.
As AI capabilities evolve rapidly, organizations accelerate adoption to seize the technology’s transformative potential. Yet, this swift progress also brings escalating security challenges. Threat actors leverage AI to launch more sophisticated attacks, amplifying the need for vigilant defenses.
As organizations race to harness AI’s potential, they must adopt proactive security measures and adaptive defense strategies to navigate an increasingly complex digital landscape. Global deregulation fuels rapid AI innovation, sparking an international arms race. Nations, from government-funded labs in China to tech giants in Silicon Valley, are racing to develop advanced AI capabilities to secure a strategic edge.
According to Delinea’s Cybersecurity and the AI Threat Landscape report, AI-powered threats surged in 2024. Tactics like AI-generated phishing and deepfake fraud bypassed traditional defenses, while info-stealer malware enabled large-scale breaches, as seen in the Snowflake incident. The growing reliance on machine identities expanded the attack surface, and poor credential rotation left systems exposed.
This dual-use nature of AI highlights the delicate balance between progress and security. In this fast-moving environment, organizations face an unintended consequence: shadow AI. This refers to the unsanctioned use of AI tools by teams without approval or oversight from IT or security departments.
The easy availability of free or low-cost AI tools exacerbates the issue, allowing teams to bypass traditional procurement channels with built-in compliance controls. These rogue AI projects address business needs without assessing risks, exposing sensitive data to breaches or compliance violations. Operating outside IT infrastructure, they evade security monitoring, increasing exploitation risk.
For regulated industries like finance, healthcare or defense, shadow AI can lead to GDPR and HIPAA violations, resulting in fines, legal consequences and reputational damage. The challenge of shadow AI is not merely technical but also an organizational and cultural issue. In many organizations, the rapid pace of innovation creates a "move fast and break things" mentality.
While this can be beneficial in fostering creativity and agility, it also leads to environments where best practices are overlooked. Teams may prioritize short-term gains over long-term risk management, inadvertently setting the stage for future vulnerabilities. Addressing shadow AI requires a comprehensive approach that combines visibility, governance and proactive threat detection.
Organizations can mitigate risk and strengthen resilience by focusing on five things: Robust, enforceable policies are the backbone of secure AI adoption. They define acceptable use, mandate risk assessments and ensure privacy, bias and security standards compliance. Clear development, deployment and governance guidelines help organizations unlock AI’s potential—driving innovation and efficiency.
Organizations should create a cross-functional board to make fast decisions regarding using new tooling and AI models so that governance can move at the speed of innovation. With policies in place, the next step is gaining complete visibility into AI usage across the organization. Teams often adopt AI tools without IT or security approval, introducing hidden vulnerabilities.
Implementing systems that automatically discover and inventory AI tools—both sanctioned and unsanctioned—helps organizations map their AI landscape, enforce policies and identify processes or bots operating outside approved channels. Once AI deployments are identified, enforcing strict access and identity controls is essential. Integrating AI tools into the broader IT security framework ensures only authorized users and systems can access sensitive data.
This includes implementing role-based access controls (RBAC), enforcing least privilege policies and continuously managing access credentials to prevent unauthorized use or credential sprawl. Even with visibility and policy controls in place, continuous monitoring is vital. Automated governance tools can track usage patterns and analyze AI behavior to detect anomalies—such as unusual access attempts or unexpected data flows—that may indicate security breaches or misuse.
Real-time alerts enable security teams to investigate and mitigate threats immediately, reducing the potential impact of shadow AI. Organizations must educate employees about the risks associated with unauthorized AI tool usage to prevent the proliferation of shadow AI. Regular training on AI best practices, security protocols and policy adherence promotes a culture of responsible AI adoption and ensures compliance.
Solutions like privileged access management (PAM) and identity security tools are critical to a robust AI governance strategy. All AI connections are privileged connections. AI robots connect to sensitive data sources to read, write and export, as well as to workflow systems for actions.
With the rise of agentic AI, privileged control of AI identities becomes even more critical as hundreds or thousands make independent decisions of AI process controllers. PAM ensures access is authenticated, authorized and audited, closing loopholes exploited by shadow AI. Privileged access controls support a “rapid innovation” mindset, where speed and agility are critical for competitive advantage.
Automated credential life cycle management allows organizations to securely provision, rotate and decommission credentials for AI bots and dynamic workloads, minimizing human error and ensuring access policies evolve with development cycles. By embedding security into the innovation process, businesses can scale AI initiatives confidently without sacrificing safety or compliance, turning access management into an enabler, not a bottleneck for progress. As AI evolves at breakneck speed, businesses must balance innovation with security.
This balance is crucial, as risks can quickly overshadow AI’s benefits. Organizations that succeed in striking this delicate balance will be best positioned to lead in the AI-driven future. In the race for AI supremacy, businesses that navigate evolving threats with agility and foresight will drive innovation while setting the standard for responsible AI deployment.
Security isn’t just a safeguard—it’s an innovation catalyst. An identity-centric approach empowers organizations to experiment confidently, accelerating development and boosting productivity. In this way, security transforms from a constraint into a competitive edge.
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