#90 — Cold Take: Companies are stuck in "AI pilot hell"

Implement use case-specific testing rather than generic safety measures. Generate nefarious test cases automatically (don't write them manually), test your base models first, then add external guardrails, then configure them for your specific use case. One global banking customer went from unacceptable security levels to production-ready through this iterative process, testing over 800 different models to optimize for both security and cost.

Security testing typically adds around $50 per testing cycle - minimal compared to potential savings. The same bank that tested 800 models actually reduced costs by identifying smaller, safer models that performed as well as larger ones. You're not just paying for security; you're optimizing your entire model portfolio.

You're stuck in 'pilot hell' because demos don't address enterprise integration challenges. Real production requires securely connecting AI orchestration frameworks to legacy databases, ensuring multi-agent coordination without vulnerabilities, and building compliant data pipelines. BMO successfully moved AI into production by focusing on risk management and KPIs rather than just proof-of-concept functionality.

Treating AI like traditional software instead of understanding AI-specific attack vectors. Even authorized users can manipulate AI systems to reveal confidential information. Traditional cloud security posture management ignores the AI component entirely - you need both traditional cybersecurity AND AI-specific security measures.

Show them factual security metrics through iterative testing, not vendor rhetoric. CISOs with 20-30 years of experience want proven security protocols. Demonstrate your security testing process: test base models, add external guardrails, configure specifically for your use case, then maintain ongoing monitoring. Cisco's acquisition of Robust Intelligence and Palo Alto Networks' acquisition of Protect AI show market leaders taking AI security seriously.

The integration nightmare makes this a critical decision. Building in-house requires expertise in enterprise architecture, data engineering, cybersecurity, AND agentic AI - skills that don't exist in most teams. The AI talent gap affects 50% of available positions, meaning for every two AI security positions posted, only one can be filled with qualified candidates.

Immediate cost optimization plus long-term risk mitigation. Companies testing across model portfolios often discover smaller models that are as safe or safer than larger ones while being significantly cheaper. Plus, 92% of companies plan to increase AI investments over the next three years - proper security testing now prevents costly security incidents later.

Continuously, not as a one-time assessment. Models are non-deterministic and run in cloud environments with components that change outside your control. Think of it like traditional software security - you need ongoing monitoring because the threat landscape and model behavior evolve constantly. One bank maintains continuous testing across 800 different models in their portfolio.

Content safety filters and guardrails alone are not good enough and won't be anytime soon. Real AI security requires layered approaches specific to your use case. A healthcare company's patient-facing AI has completely different security requirements than a travel company's internal research tool. Generic content safety policies miss the nuances of your actual business risks.

They're racing for market dominance, hoping something sticks with mass users. But enterprise leaders with decades of experience demand proven security protocols before allowing AI into their environments. Enterprise AI adoption is only 10% despite a projected $4 trillion market opportunity - the security gap is the primary bottleneck preventing that $3.6 trillion in unrealized value.

Implement AI-agnostic runtimes that let you switch models based on security, performance, and cost metrics. This flexibility is crucial because AI technology moves fast both politically and technologically. BMO's success comes from being able to measure across dimensions - cost, performance, security, and safety - then switch models as needed while maintaining security standards.

You need expertise at the intersection of enterprise architecture, data engineering, cybersecurity, and agentic AI. This isn't just connecting APIs - it's complex, multi-disciplinary integration work. Most internal teams aren't equipped for securely connecting modern orchestration frameworks to decade-old legacy databases while ensuring multi-agent coordination doesn't create vulnerabilities.

Key security challenges include exposure of sensitive information, AI model manipulation through adversarial or prompt injection attacks, data poisoning, gaps in traditional cybersecurity for AI-specific risks, and lack of continuous security testing. 88% of AI pilots fail to reach production primarily due to expanded attack surfaces when integrating AI into legacy systems, requiring multilayered, use-case specific security frameworks to move from pilot to production securely.

AI pilots fail mainly due to lack of integration with existing infrastructure, absence of continuous security and compliance testing, incomplete risk management, chaotic internal adoption, and talent shortages. Pilots often succeed under ideal, isolated conditions but can't withstand real-world production demands without mature governance and security processes. Only 13% of companies are ready to scale AI beyond experimental phases.

Continuous AI security testing typically costs around $50 per cycle and can reduce overall expenses by identifying smaller, cheaper, and safer models. One bank evaluated 800 different models to optimize for both security and cost, often finding smaller models as safe or safer than larger ones. It's a cost-effective investment compared to potential breaches, operational failures, or regulatory penalties.

Both must tackle complex challenges of securely connecting new AI systems to legacy databases, coordinating multi-agent AI without vulnerabilities, building compliant data pipelines, and managing AI lifecycle with skilled teams. This convergence of enterprise architecture, cybersecurity, data engineering, and AI science requires strategic partnerships and proven methodologies. 74% of companies struggle to achieve scalable value from AI integration, making this the single greatest service opportunity in the current market.

Best practices involve use case-specific security definitions rather than generic safety measures, automated generation of nefarious test cases (don't write them manually), iterative validation cycles that don't trust vendor claims, layered security controls, and ongoing continuous testing in CI/CD pipelines. Test your base models first, then add external guardrails, then configure specifically for your use case - this approach helped one global banking customer go from unacceptable to production-ready security levels.

CISOs and risk officers require factual, iterative security metrics demonstrating maturity, reliability, and safety of AI models rather than vendor rhetoric. Show them proven security testing processes with measurable outcomes. Highlight real-world examples from market leaders like Cisco's acquisition of Robust Intelligence and Palo Alto Networks' acquisition of Protect AI, demonstrating that established cybersecurity companies are taking AI security seriously through strategic investments.

AI security testing should be continuous, not one-time assessments. Models are non-deterministic and run in cloud environments with components that change outside your control. Think of it like traditional software security - ongoing monitoring is essential because threat landscapes and model behavior evolve constantly. Content safety filters and guardrails alone are not good enough and won't be anytime soon.

Employees are more ready and optimistic about AI than leaders realize. 94% of employees report familiarity with AI tools, and they're three times more likely than leaders think to believe AI will replace 30% of their work within a year. 47% want more formal training, making employee support - not employee resistance - the key to successful adoption. Millennials aged 35-44 are natural AI champions with 62% reporting high expertise levels.

AI security uniquely deals with model-specific risks like prompt injections, model theft, data poisoning, and adversarial attacks that traditional cybersecurity doesn't address. Even authorized users can manipulate AI systems to reveal confidential information. Traditional cybersecurity focuses on network and endpoint control but often ignores internal AI logic manipulation. You need both traditional cybersecurity AND AI-specific security measures - layered approaches beyond basic content filters.

Barriers include protecting sensitive data with complex access controls, enforcing regulatory compliance across AI tools, ensuring scalability and security, overcoming workforce skill gaps (50% of AI positions can't be filled with qualified candidates), and managing costly, unpredictable AI infrastructure needs. The integration nightmare requires expertise at the intersection of enterprise architecture, data engineering, cybersecurity, and agentic AI - skills most internal teams lack.

AI vendors are racing for market dominance, hoping for mass adoption before competitors establish market share. But enterprise leaders with decades of experience demand proven security protocols before allowing AI into their environments. This creates a fundamental mismatch: enterprise AI adoption is only 10% despite a projected $4 trillion market opportunity. The $3.6 trillion gap represents unrealized value blocked primarily by security concerns rather than technical limitations.

Implement AI-agnostic runtimes that let you switch models based on security, performance, and cost metrics. This flexibility is crucial because AI technology moves fast both politically and technologically. Use modular approaches and federated governance models that allow you to evaluate across dimensions - cost, performance, security, and safety - then switch models as needed while maintaining security standards. Avoid putting all eggs in one major closed shop model vendor.