AI as an Insider Threat: Expanded Risks with Expanded Usage
Next-generation AI models may pose a “high” cybersecurity risk, including the potential to generate sophisticated exploits or assist intrusion operations, according to a warning from OpenAI. This highlights that AI is no longer just a defensive tool; it is a strategic attack surface that organizations must actively govern. Adding to that, 60% of organizations are highly concerned about employee misuse of AI tools enabling insider threats, according to the 2025 Insider Risk Report by Cybersecurity Insiders and Cogility. The Problem: AI Expands the Attack Surface AI is now embedded across workflows in engineering, HR, finance, and clinical operations, introducing new and often misunderstood risks. Modern AI systems can generate executable code, identify vulnerabilities, craft personalized phishing messages, and automate reconnaissance. While these tools support security teams, they also enable misuse by anyone with access. Employees, contractors, or third parties may unintentionally or deliberately misuse AI in ways traditional controls can miss. Shadow AI use, unapproved model integrations, and poorly governed prompts increase exposure. Compounding the issue, 62% of organizations faced at least one deepfake attack in the past year, and 32% experienced attacks targeting their AI applications, according to a 2025 Gartner survey. Why This Matters: Business, Regulatory, and Board Risk AI-driven insider risk is a strategic business threat. Misuse can corrupt data, create vulnerabilities, disrupt operations, and expose sensitive information, impacting productivity, customer trust, and competitive advantage. Regulatory risks are growing as organizations must comply with HIPAA, SOX, GDPR, and new AI governance requirements focused on transparency and accountability. Noncompliance risks costly enforcement and reputational harm. Boards now expect CISOs and CIOs to clearly explain how AI-enabled insider risks are governed and mitigated. Failure to do so may weaken investor confidence and slow strategic initiatives. How Organizations Can Prepare Organizations should adopt a holistic People, Process, Technology approach. People: Train employees and leaders on responsible AI use and insider risk awareness. Process: Implement strong AI governance frameworks defining acceptable use, model oversight, and ownership. Incorporate AI-specific threat modeling and behavioral analytics, leveraging standards like the NIST AI Risk Management Framework. Technology: Enforce identity and access controls around AI tools and sensitive data, along with continuous monitoring to detect misuse early. Finally, investing in ongoing employee education is critical because even as AI evolves, people remain the backbone of any defense strategy. AccessIT Group’s Strategy and Transformation practice helps organizations design and implement AI-aware insider risk programs that align strategy, governance, and technology controls to protect sensitive data and enable secure, value-driven AI adoption.
Is the Cloud Migration Mindset Snafu Reoccurring with Untethered AI Adoption?
Organizations once rushed to the cloud in search of transformation, innovation, reduced cost of ownership, and a competitive advantage. In that haste, they overlooked a hard truth: threat actors thrive in environments filled with misconfigurations and weak security practices. Many enterprises quickly embraced cloud capabilities, but they failed to bring cybersecurity along with them. Most organizations never thoroughly answered the foundational question of cloud-era security: Where does our critical data reside? Even now, many enterprises lack a complete inventory of sensitive data locations or data flows. That visibility gap did not disappear; it simply shifted. And now, with the rise of GenAI, that same unknown data is being fed into tools outside organizational control. The result was years of avoidable breaches, exposed buckets, overly permissive identities, and reactive security strategies that continue to ripple across the industry today. We are witnessing the same pattern with Generative AI and LLMs. The rapid introduction of GenAI and large language models has created unprecedented opportunities, rapid innovation, resource optimization, improved productivity, and enhanced decision quality. Yet one issue persists: Where are the guardrails? For most organizations, they are either immature or nonexistent. AI Governance should have been implemented from day one, with oversight committees established early to set boundaries, evaluate risks, and shape responsible adoption. To bridge this gap, organizations should define clear roles, responsibilities, and processes for these committees to ensure continuous oversight and accountability. This proactive approach helps organizations embed governance into their AI strategies from the outset, reducing risks and aligning with best practices. This is not speculation. Recent research shows that employees are adopting Generative AI at extraordinary rates, often without informing IT or leadership. A supporting perspective can be found in this post by Ian Paul of CheckPoint, ‘How CIOs Can Turn AI Visibility into Strategy.’ The implications are significant. Hidden or “shadow” AI usage creates an environment in which innovation occurs organically, but without governance, oversight, or security. Yet that same usage data, when finally observed, can become an invaluable blueprint for formulating an informed AI strategy. Organizations can learn exactly which tools employees find valuable and which workflows are ripe for meaningful AI-driven efficiency gains. But visibility is the prerequisite for strategy. Security leaders need to understand which AI services are being accessed, what types of prompts are being submitted, how much sensitive content is being shared, and where risky behavior is occurring. Implementing monitoring tools such as AI activity dashboards, data flow analysis, and real-time alerting can provide the necessary visibility. These methods enable organizations to identify unauthorized AI usage, assess data exposure, and ensure compliance with security policies, thereby supporting a more informed and secure AI environment. The gap between organizational intent and real-world usage shows why AI Governance must be a core function, giving leaders confidence in responsible AI management. The lesson is clear. Building visibility, governance, and accountability into AI adoption helps organizations feel prepared and secure against repeating past mistakes. Organizations do not need to slow down innovation. They need to ensure that innovation does not outpace cybersecurity’s ability to support it safely.