Why 82% of Companies Think They’re Secure — But Aren’t

Enterprise AI adoption is accelerating at breakneck speed, creating both unprecedented opportunities and hidden challenges that every technology leader should understand. New research from Anaconda surveying over 300 AI practitioners and decision-makers reveals a critical gap between perception and reality: while 82% of organizations believe they have robust processes to validate Python packages and dependencies for security compliance, nearly 40% still regularly encounter security vulnerabilities in their AI projects. Even more telling: 67% experience deployment delays due to security issues.

This isn’t just a numbers game—it’s a strategic opportunity to bridge the gap between what organizations think they have versus what they actually need to unlock AI’s full potential.

The Confidence Gap: Where Opportunity Meets Reality

Our recent research reveals what forward-thinking leaders are beginning to recognize: there’s a significant opportunity in the gap between AI governance policies and actual practice. Organizations are implementing layered security approaches—70% use automated scanning tools, 61% maintain internal package registries, 57% conduct manual reviews—yet security vulnerabilities in open-source components remain the most common risk in AI development.

The issue isn’t a lack of awareness. Leadership teams are tracking compliance metrics, with 58% measuring adherence to regulations as a key performance indicator. The real opportunity lies deeper: we’re applying yesterday’s governance frameworks to tomorrow’s AI challenges, creating untapped potential for those who get it right.

Think of it this way—governance isn’t your organizational seatbelt. It’s your steering wheel. And right now, the organizations that master integrated governance will have a significant competitive advantage.

When Innovation Meets Implementation Challenges

AI systems are fundamentally different beasts. They’re constructed on top of vast, fast-moving supply chains of open-source code, cloud platforms, and third-party models. The governance challenges extend beyond the models themselves to encompass the data that powers them—from training datasets to fine-tuning processes that shape model behavior. Unlike static applications, AI models drift, degrade, and behave unpredictably when exposed to new data. Yet we’re often treating them like traditional software deployments.

Our earlier research emphasizes this interconnected reality: 57% of organizations cite regulatory compliance and data privacy concerns as top challenges, while 45% struggle with accessing high-quality domain-specific training data. The governance gap isn’t just about securing models—it’s about securing the entire AI development lifecycle, from data ingestion through model deployment.

Consider what happened at a major entertainment company earlier this year. A single malicious file disguised as an AI art generation tool led to the compromise of 1.1 terabytes of confidential data from thousands of internal communication channels. The attack succeeded not because of sophisticated techniques, but because supply chain security practices weren’t integrated into everyday workflows – and because data governance and model governance were treated as separate concerns rather than interconnected risks.

Three critical gaps represent the biggest opportunities for competitive advantage:

  • Toolchain Fragmentation – Only 25% of organizations describe their AI toolchain as “highly unified” across the model lifecycle. The majority are grappling with fragmented systems where the right integration strategy could unlock significant efficiency gains. When data scientists download packages in siloed environments, security teams often don’t know until deployment—but organizations solving this coordination challenge are seeing measurable productivity improvements.
  • Documentation as Strategy – While 83% claim they track the origins of foundation models, nearly one in five organizations still have no formal documentation of model dependencies or lineage. The organizations that master comprehensive model lineage are positioning themselves for regulatory compliance advantages and faster incident response.
  • Monitoring as Competitive Intelligence – Despite 70% of teams reporting mechanisms to monitor model performance, 30% have no formal drift monitoring at all. This represents a huge opportunity: organizations with robust monitoring systems can detect performance issues, optimize models proactively, and maintain service quality that competitors can’t match.

There’s No Magic Framework (And That Creates Opportunity)

In the early days of the Internet, downloading a single image took 30 minutes. Building a basic website lasted weeks. There wasn’t one standard for everything. We iterated as we went and accepted that uncertainty because we understood what the web could unlock.

We’re in a similar moment of opportunity with AI governance.

Right now, there’s no magic bullet. No single framework works for every company, every team, or every use case. AI governance is about intentionality—understanding the different dimensions of your needs and building systems that enable safe, domain-specific exploration. This early stage means competitive advantages are available to organizations that act thoughtfully now.

The solution isn’t more tools—it’s better integration. When we asked practitioners what would most improve their AI governance, the top answer was clear: integrated tools that combine development and security workflows. Better visibility into dependencies and more team training followed closely.

The common thread? Transparency that enables velocity.

From Reactive Patching to Strategic Advantage

About three in four companies have fragmented toolchains. That’s manageable—if you understand why and maintain visibility across the pieces. The organizations that master this coordination are seeing measurable returns: faster deployment cycles, fewer security incidents, and more predictable AI outcomes.

Governance challenges create competitive differentiation opportunities. When AI systems make decisions without proper accountability frameworks, some organizations stumble while others excel. Airlines, healthcare systems, financial services—the companies with robust governance frameworks are the ones scaling AI successfully while others face costly setbacks.

Effective AI governance means shifting from “what can’t we do” to “what should we do, and how do we do it at scale.” This means:

  • Embedding Governance in Workflows – Rather than layering security on top of existing processes, governance must be woven into everyday development workflows. Teams need environments where doing the right thing—secure, compliant, well-monitored AI—is easier than taking shortcuts.
  • Cross-Functional Alignment – Get the most open-minded people from legal, compliance, and data stewardship to co-create governance alongside the builders. Grow that trust by having teams ride together, not bolt governance on after the fact.
  • Continuous Monitoring – AI models must be observed continuously, not just at deployment. This means automated alerts for drift, performance degradation, and anomalous behavior—treating models like the live systems they are.

Building the Discipline for Long-Term Advantage

The AI landscape evolves constantly through regulatory changes, compliance requirements, and licensing uncertainties. In this environment, governance helps teams operate quickly while building sustainable competitive advantages.

If your teams deploy projects with significant AI components, expect to write more monitoring, tests, and validation suites. “Does it work?” is no longer sufficient. The winning question becomes: “Can we explain why it works, scale it reliably, and optimize it continuously?”

Organizations that succeed will be those that treat governance as an emerging discipline worth investing in for strategic advantage. They’ll allocate time for professional development, experimentation, and building real guardrails at both business and technology levels. Governance and experimentation aren’t competing priorities—they must be embedded together at the team level to create sustainable innovation velocity.

The Strategic Imperative

The governance opportunity isn’t just manageable—it’s a pathway to AI leadership. Traditional approaches aren’t sufficient for AI’s unique challenges, but that creates an opening for organizations ready to invest in next-generation governance practices.

The choice is clear: invest in comprehensive AI governance proactively, or reactively after competitors gain advantage. Organizations building comprehensive evaluation frameworks now, combining performance metrics with security considerations, are positioning themselves for sustained AI leadership.

The data tells the story: 82% confidence built on fragmented practices isn’t confidence at all—it’s an opportunity waiting for the right strategy. The teams building governance capabilities now will be the ones who discover what their organizations can truly accomplish with AI, turning governance challenges into competitive differentiation.

In the AI era, that difference will define market leaders.

Download Anaconda’s full research report, “Bridging the AI Model Governance Gap,” to explore detailed findings from over 300 AI practitioners and discover actionable strategies for building comprehensive AI governance that accelerates rather than inhibits innovation.