Smart AI adoption for IT: Addressing security, compliance, and vendor risks

AI adoption in IT has now moved from theory to practice, becoming an essential part of daily operations. With promises of increased productivity, faster decision-making, and streamlined operations, IT teams are under pressure from management to integrate AI into their workflows. However, behind these opportunities lie significant challenges in security, compliance, and vendor risk management, and failure to address these hurdles can undermine the value AI can deliver.

While AI can automate routine tasks, enable smarter insights, and reduce operational bottlenecks, poorly managed implementation can expose organizations to serious vulnerabilities. Security breaches, compliance failures, and vendor mismanagement aren’t just IT issues—they’re business risks that can damage reputation, erode customer trust, and lead to costly regulatory penalties. Overcoming these hurdles requires a strategic approach, blending technical expertise with strong governance and clear alignment with business goals.

Security: Managing data, access, and transparency

Ryan Kazanciyan, CIO and CISO of Wiz, on the AI risks people should focus on:

One of the most significant risks tied to AI adoption revolves around data security. AI tools rely on vast amounts of data to function effectively, and this data often includes sensitive company or customer information. Mismanagement of AI systems—whether through poor access controls, insufficient encryption, or inadequate governance—can expose organizations to breaches, data leaks, or compliance failures.

AI tools are often integrated across multiple systems, pulling data from different sources and interacting with a variety of platforms. Each connection point creates potential vulnerabilities, and these risks multiply as more tools are added to the ecosystem. For example, an AI tool integrated into IT ticketing systems might inadvertently expose sensitive infrastructure data if misconfigured.

To mitigate these risks, IT leaders must prioritize a “data-first” security approach:

• Access control: Ensure that only authorized personnel have access to AI tools and the data they handle.
• Data encryption: Use end-to-end encryption for all data exchanges involving AI systems.
• Monitoring and auditing: Regularly review AI tool logs and access trails to detect and address any anomalies.
• Governance protocols: Establish clear policies for how AI tools can share, store, and access data.

Moreover, organizations must train employees to understand the risks associated with AI tools and adopt safe practices when handling sensitive data. Security isn’t just a technical responsibility—it’s an organizational mindset.

Ryan Kazanciyan, CIO and CISO of Wiz, on how to manage third-party SaaS risks with employees:

Regulation and compliance: A moving target

The regulatory landscape for AI is still evolving, and organizations are often left navigating ambiguous or inconsistent compliance requirements. While regulations vary across regions and industries, there are a few universal key principles: Transparency, accountability, and responsible AI use.

However, many IT teams are buried in endless third-party security questionnaires and vendor assessments. While intended to ensure compliance, these processes often add unnecessary friction without effectively reducing risk. For example, some compliance checklists focus on AI’s theoretical risks rather than the practical vulnerabilities posed by data mismanagement, integration flaws, or inadequate oversight.

Ryan Kazanciyan, CIO and CISO of Wiz, on the need for uniform AI risk assessment:

To address these challenges, IT teams should focus on:

• Dynamic compliance frameworks: Build adaptable compliance processes that can evolve with changing regulations without necessitating the building of new processes from the ground up each time.
• Clear vendor alignment: Partner with vendors who demonstrate adherence to established compliance standards such as GDPR, SOC 2, and ISO 27001.
• Purpose-driven assessments: Prioritize understanding how AI vendors handle data sharing, storage, and lifecycle management instead of chasing exhaustive documentation.

Compliance should enable AI adoption, not stifle it. Organizations that embed compliance principles into their AI strategy from the outset will avoid costly retrofitting later.

Vendor risk management: Avoiding complexity overload

The AI vendor ecosystem is booming, with countless platforms and SaaS providers offering AI-powered features. While the variety of tools can be exciting, integrating too many point solutions often leads to bloated tech stacks, overlapping functionalities, and hidden costs.

Vendor risk management in the AI era is not just about technical assessments—it’s about strategic alignment. IT leaders must ask:

• Do we actually need this tool? Could an existing platform offer similar functionality without adding another vendor?
• What are the long-term costs? Beyond licensing fees, consider integration, training, and support costs.
• Is the ROI clear? Vendors with transparent, success-based pricing models reduce financial risk and make experimentation more accessible.
• How does it fit into our ecosystem? Ensure that the vendor’s AI solution integrates seamlessly with existing tools and workflows.

In many cases, AI features are already bundled into widely-used enterprise platforms. Instead of adding standalone tools for every AI use case, IT leaders should evaluate whether their current systems can meet their needs with fewer disruptions. Consolidated platforms reduce vendor sprawl, simplify governance, and create a more manageable risk profile.

Ryan Kazanciyan, CIO and CISO of Wiz, and Tal Dagan, Atera’s CPO, on how to approach AI integration:

Balancing innovation with risk: A practical roadmap for AI adoption

Successfully overcoming AI adoption hurdles requires a strategic and iterative approach. Below are five practical steps to guide IT leaders through the process:

1. Centralize AI governance: Establish a cross-functional governance team with representatives from IT, security, compliance, and legal. This team should oversee AI initiatives, address risks, and ensure alignment with business goals.
2. Prioritize secure integration: Adopt a zero-trust approach when integrating AI tools into existing systems. Ensure that every data flow is monitored and every integration point is secured.
3. Adopt a pilot-first approach: Start with smaller AI implementations to test effectiveness and measure ROI before scaling organization-wide.
4. Evaluate total cost of ownership (TCO): Look beyond upfront licensing fees. Consider integration costs, employee training, and ongoing vendor support.
5. Foster a culture of experimentation and accountability: Encourage teams to explore AI capabilities, but with clear oversight and defined boundaries.

Innovation thrives when risk is managed—not eliminated. Organizations that embrace AI experimentation, while keeping a sharp focus on security and compliance, will be better positioned for long-term success.

The path forward: Smarter AI adoption

Overcoming AI adoption hurdles isn’t about eliminating every risk—it’s about managing them intelligently. Organizations that succeed in addressing security vulnerabilities, aligning with compliance standards, and managing vendor relationships strategically will be well-positioned to harness AI’s full potential without adding risk.

IT leaders must move away from reactive approaches and embrace a proactive mindset. Security should be embedded into every stage of the AI lifecycle, compliance should be viewed as an enabler rather than an obstacle, and vendor relationships should prioritize strategic alignment over short-term gains.

The teams that get this balance right will not only maximize productivity but also create resilient, future-ready IT operations capable of navigating the ever-evolving digital landscape.

AI adoption isn’t a finish line—it’s an ongoing journey. And for IT leaders, the path forward lies in smarter decisions, clearer priorities, and a relentless focus on delivering value at every stage.

Secure AI adoption with Atera

Tal Dagan, Atera’s CPO, on Atera’s approach to secure AI:

Atera’s all-in-one IT management platform provides IT teams with the tools to adopt AI securely and effectively. With built-in governance features, end-to-end encryption, and alignment with global compliance standards, Atera simplifies complex processes and reduces risk at every stage of AI integration.

From managing vendor relationships to maintaining data security and ensuring compliance, Atera equips IT leaders to confidently harness AI effortlessly. Explore how Atera can support your AI adoption strategy your AI adoption strategy by requesting a demo today.