Enterprise adoption of generative AI (GenAI) is accelerating at a pace far beyond previous technological advances, with organizations using it for everything from drafting content to writing code. It has become essential for mission-critical business functions, but with increased AI adoption comes an increasing risk that remains poorly understood or inadequately addressed by many organizations. Security, bias mitigation and human oversight are no longer afterthoughts. They are prerequisites for sustainable, secure AI deployment. 

The Expanding Attack Surface

The most well-known GenAI vulnerabilities relate to prompt injection, in which attackers manipulate inputs to bypass safeguards, leak sensitive data or trigger unintended outputs, but it is only the beginning. With open-ended, natural-language interfaces, GenAI creates a fundamentally different attack surface from traditional software. 

Additionally, there is no such thing as set it and forget it in security, so organizations like Lenovo are adapting “Secure by Design” frameworks that evolve for products and services. GenAI is the next important consideration in the new security approach, requiring new safeguards throughout the implementation lifecycle—from initial data ingestion through deployment and continuous monitoring. Organizations must also revisit data classification, as existing high-level practices are limited. Without fine-grained categorization and appropriate data labeling, access controls break down—especially with large models that often require broader data access to operate effectively. 

This challenge compounds in agent-to-agent systems, in which autonomous AI agents interact and pass information. These systems present unique challenges as their autonomous decision-making and interconnected workflows amplify risk. Every agent interaction introduces new attack surfaces and threats such as data leakage, privilege escalation and adversarial manipulation, which can cascade quickly across linked systems, causing failures, compounding errors and distributing misinformation at machine speed. All these risks can evolve too quickly for conventional monitoring to catch—unless humans remain in the loop from setup through deployment and conduct regular system checks. 

Bias, Trust and Governance 

As damaging as a data leakage incident can be, the long-term risks far surpass the short-term pain. Biased outputs undermine trust, misinform stakeholders and erode brand reputation—not to mention putting organizations that operate in highly regulated industries like healthcare and banking at significant risk of penalties for being out of compliance. As a result, organizations must emphasize responsible and ethical AI, embedding governance into every layer of the AI lifecycle and evaluating through that lens every step of the way. 

Adhering to best practices in governance requires three main requirements: 

• Trusted Data Sources: Models must only be trained and prompted with reliable, verified inputs. This is the classic adage of “garbage in, garbage out,” which highlights the previously discussed need for proper data categorization and labeling. It also reduces the possibility of hallucinations and lowers leakage risk.  

• Framework-Level Guardrails: When considering an AI implementation framework, guardrails must be established at the outset and carried all the way through, applying validation at multiple layers: ingestion, model behavior and outputs. Otherwise, organizations with potentially unsafe data practices risk compliance ramifications.  

• Ongoing Testing: As machines acquire more data for training and inferencing, processes and outputs will change accordingly, making it crucial that organizations continuously assess pre- and post-deployment to detect bias and drift—both of which negatively impact output quality and place organizational reputation at risk. 

With these three best practices in mind, organizations can establish a true governance-first mindset that aligns with the principles many security-first organizations already follow. AI must be unbiased, transparent, explainable and secure for both organizations and end users. Again, the human in the loop becomes critical, as automation alone cannot achieve this. Trained reviewers must validate outputs before they are operationalized—especially in regulated or high-impact industries. 

Closing the Maturity Gap 

While most organizations recognize the risks of GenAI, they also lack the maturity models, training, or tools to operationalize its security. Often, they stop at pre-launch checks, when in reality GenAI security demands end-to-end vigilance across the full lifecycle—akin to a zero trust solution authenticating users and devices at every step of access. 

Operationalizing this full lifecycle visibility and governance requires a few best practices: 

• Train beyond technical teams: To responsibly deploy AI, organizations must establish a security-first mindset across business functions, ensuring all leaders buy in and adhere to best practices in prompt hygiene and data sensitivity. 

• Test models continuously: Akin to recurring software patches, models must undergo continuous review. Furthermore, these evaluations must cover the entire deployment lifecycle.  

• Integrate DevSecOps: A corollary of training all business functions to operate with a security-first mindset, organizations must enforce it with technical teams as well by embedding it directly into development pipelines. 

• Review access practices: Just as models must be tested continuously, access must also be evaluated with organizations adopting and enforcing least privilege to ensure that only the right systems and people in the right roles have access to the right information. 

• Automate data labeling—with oversight: Data labeling is a massive undertaking that benefits greatly from the efficiency of AI tools to accelerate classification, but establishing context requires human validation. 

• Simulate incident and response: Best practices in security, like tabletop exercises and clear accountability, apply to a GenAI breach like any other critical threat vector, but with AI’s ability to rapidly proliferate an incident, the stakes are considerably higher. 

Trust as the Foundation 

Organizations of all types have bought into the transformative opportunities GenAI offers, but many are ill-equipped for the security requirements that will come with realizing its full potential. Only those that establish a security-first culture that permeates the entire organization—prioritizing transparent supply chains and lifecycle governance—will have the embedded trust in their foundations that positions them to safely and securely deploy GenAI.  

In this next phase of AI maturity, adoption alone is not enough. Organizations must secure, govern and validate at every step. Innovation may spark adoption, but trust sustains it.