What misbehaving AI can cost you

AI security is no longer optional. By 2026, organizations that fail to infuse transparency, trust, and security into their AI initiatives could see a 50% decline in model adoption, business goal attainment, and user acceptance – falling behind those that do.

At the same time, AI leaders are grappling with another challenge: rising costs.

They’re left asking: “Are we investing in alignment with our goals—or just spending more?”

With the right strategy, AI technology investments shift from a cost center to a business enabler — protecting investments and driving real business value.

The financial fallout of AI failures

AI security goes beyond protecting data. It safeguards your company’s reputation, ensures that your AI operates accurately and ethically, and helps maintain compliance with evolving regulations.

Managing AI without oversight is like flying without navigation. Small deviations can go unnoticed until they require major course corrections or lead to outright failure.

Here’s how security gaps translate into financial risks:

Reputational damage

When AI systems fail, the fallout extends beyond technical issues. Non-compliance, security breaches, and misleading AI claims can lead to lawsuits, erode customer trust, and require costly damage control.

• Regulatory fines and legal exposure. Non-compliance with AI-related regulations, such as the EU AI Act or the FTC’s guidelines, can result in multimillion-dollar penalties.

  Data breaches in 2024 cost companies an average of $4.88 million, with lost business and post-breach response costs contributing significantly to the total.

• Investor lawsuits over misleading AI claims. In 2024, several companies faced lawsuits for “AI washing” lawsuits, where they overstated their AI capabilities and were sued for misleading investors.
• Crisis management efforts for PR and legal teams. AI failures demand extensive PR and legal resources, increasing operational costs and pulling executives into crisis response instead of strategic initiatives.
• Erosion of customer and partner trust. Examples like the SafeRent case highlight how biased models can alienate users, spark backlash, and drive customers and partners away.

Weak security and governance can turn isolated failures into enterprise-wide financial risks.

Shadow AI

Shadow AI occurs when teams deploy AI solutions independently of IT or security oversight, often during informal experiments. 

These are often point tools purchased by individual business units that have generative AI or agents built-in, or internal teams using open-source tools to quickly build something ad hoc.

These unmanaged solutions may seem harmless, but they introduce serious risks that become costly to fix later, including:

• Security vulnerabilities. Untracked AI solutions can process sensitive data without proper safeguards, increasing the risk of breaches and regulatory violations.
• Technical debt. Rogue AI solutions bypass security and performance checks, leading to inconsistencies, system failures, and higher maintenance costs

As shadow AI proliferates, tracking and managing risks becomes more difficult, forcing organizations to invest in expensive remediation efforts and compliance retrofits.

Expertise gaps

AI governance and security in the era of generative AI requires specialized expertise that many teams don’t have.

With AI evolving rapidly across generative AI, agents, and agentic flows, teams need security strategies that risk-proof AI solutions against threats without slowing innovation.

When security responsibilities fall on data scientists, it pulls them away from value-generating work, leading to inefficiencies, delays, and unnecessary costs, including:

• Slower AI development. Data scientists are spending a lot of time figuring out which shields, guards are best to prevent AI from misbehaving and ensuring compliance, and managing access instead of developing new AI use-cases.

  In fact, 69% of organizations struggle with AI security skills gaps, leading to data science teams being pulled into security tasks that slow AI progress.

• Higher costs. Without in-house expertise, organizations either pull data scientists into security work — delaying AI progress — or pay a premium for external consultants to fill the gaps.

This misalignment diverts focus from value-generating work, reducing the overall impact of AI initiatives.

Complex tooling

Securing AI often requires a mix of tools for:

• Model scanning and validation
• Data encryption
• Continuous monitoring
• Compliance auditing
• Real-time intervention and moderation
• Specialized AI guards and shields 
• Hypergranular RBAC, with generative RBAC for accessing the AI application, not just building it

While these tools are essential, they add layers of complexity, including:

• Integration challenges that complicate workflows and increase IT and data science team demands.
• Ongoing maintenance that consumes time and resources.
• Redundant solutions that inflate software budgets without improving outcomes.

Beyond security gaps, fragmented tools lead to uncontrolled costs, from redundant licensing fees to excessive infrastructure overhead.

What makes AI security and governance difficult to validate?

Traditional IT security wasn’t built for AI. Unlike static systems, AI systems continuously adapt to new data and user interactions, introducing evolving risks that are harder to detect, control, and mitigate in real time. 

From adversarial attacks to model drift, AI security gaps don’t just expose vulnerabilities — they threaten business outcomes.

New attack surfaces that traditional security miss

Generative AI solutions and agentic systems introduce unique vulnerabilities that don’t exist in conventional software, demanding security approaches beyond what conventional cybersecurity measures can address, such as

• Prompt injection attacks: Malicious inputs can manipulate model outputs, potentially spreading misinformation or exposing sensitive data.
• Jailbreaking attacks: Circumventing guards and shields put in place to manipulate outputs of any existing generative solutions.
• Data poisoning: Attackers compromise model integrity by corrupting training data, leading to biased or unreliable predictions.

These subtle threats often go undetected until damage occurs.

Governance gaps that undermine security

When governance isn’t airtight, AI security isn’t just harder to enforce — it’s harder to verify.

Without standardized policies and enforcement, organizations struggle to prove compliance, validate security measures, and ensure accountability for regulators, auditors, and stakeholders.

• Inconsistent security enforcement: Gaps in governance lead to uneven application of AI security policies, exposing different AI tools and deployments to varying levels of risk.

  One study found that 60% of Governance, Risk, and Compliance (GRC) users manage compliance manually, increasing the likelihood of inconsistent policy enforcement across AI systems.

• Regulatory blind spots: As AI regulations evolve, organizations lacking structured oversight struggle to track compliance, increasing legal exposure and audit risks.

  A recent analysis revealed that approximately 27% of Fortune 500 companies cited AI regulation as a significant risk factor in their annual reports, highlighting concerns over compliance costs and potential delays in AI adoption.

• Opaque decision-making: Insufficient governance makes it difficult to trace how AI solutions reach conclusions, complicating bias detection, error correction, and audits.

  For example, one UK exam regulator implemented an AI algorithm to adjust A-level results during the COVID-19 pandemic, but it disproportionately downgraded students from lower-income backgrounds while favoring those from private schools. The resulting public backlash led to policy reversals and raised serious concerns about AI transparency in high-stakes decision-making.

With fragmented governance, AI security risks persist, leaving organizations vulnerable.

Lack of visibility into AI solutions

AI security breaks down when teams lack a shared view. Without centralized oversight, blind spots grow, risks escalate, and critical vulnerabilities go unnoticed.

• Lack of traceability: When AI models lack robust traceability — covering deployed versions, training data, and input sources — organizations face security gaps, compliance breaches, and inaccurate outputs. Without clear AI blueprints, enforcing security policies, detecting unauthorized changes, and ensuring models rely on trusted data becomes significantly harder.
• Unknown models in production: Inadequate oversight creates blind spots that allow generative AI tools or agentic flows to enter production without proper security checks. These gaps in governance expose organizations to compliance failures, inaccurate outputs, and security vulnerabilities — often going unnoticed until they cause real damage.
• Undetected drift: Even well-governed AI solutions degrade over time as real-world data shifts. If drift goes unmonitored, AI accuracy declines, increasing compliance risks and security vulnerabilities.

Centralized AI observability with real-time intervention and moderation mitigate risks instantly and proactively.

Why AI keeps running into the same dead ends

AI leaders face a frustrating dilemma: rely on hyperscaler solutions that don’t fully meet their needs or attempt to build a security framework from scratch. Neither is sustainable.

Using hyperscalers for AI security

Although hyperscalers may offer AI security features, they often fall short when it comes to cross-platform governance, cost-efficiency, and scalability. AI leaders often face challenges such as:

• Gaps in cross-environment security: Hyperscaler security tools are designed primarily for their own ecosystems, making it difficult to enforce policies across multi-cloud, hybrid environments, and external AI services.
• Vendor lock-in risks: Relying on a single hyperscaler limits flexibility, increases long-term costs, especially as AI teams scale and diversify their infrastructure, and limits essential guards and security measures.
• Escalating costs: According to a DataRobot and CIO.com survey, 43% of AI leaders are concerned about the cost of managing hyperscaler AI tools, as organizations often require additional solutions to close security gaps. 

While hyperscalers play a role in AI development they aren’t built for full-scale AI governance and observability. Many AI leaders find themselves layering additional tools to compensate for blind spots, leading to rising costs and operational complexity.

Building AI security from scratch 

The idea of building a custom security framework promises flexibility; however, in practice, it introduces hidden challenges:

• Fragmented architecture: Disconnected security tools are like locking the front door but leaving the windows open — threats still find a way in.
• Ongoing upkeep: Managing updates, ensuring compatibility, and maintaining real-time monitoring requires continuous effort, pulling resources away from strategic projects.
• Resource drain: Instead of driving AI innovation, teams spend time managing security gaps, reducing their business impact.

While a custom AI security framework offers control, it often results in unpredictable costs, operational inefficiencies, and security gaps that reduce performance and diminish ROI.

How AI governance and observability drive better ROI

So, what’s the alternative to disconnected security solutions and costly DIY frameworks?

Sustainable AI governance and AI observability. 

With robust AI governance and observability, you’re not just ensuring AI resilience, you’re optimizing security to keep AI projects on track.

Here’s how:

Centralized oversight

A unified governance framework eliminates blind spots, facilitating efficient management of AI security, compliance, and performance without the complexity of disconnected tools. 

With end-to-end observability, AI teams gain:

• Comprehensive monitoring to detect performance shifts, anomalies, and emerging risks across development and production.
• AI lineage, traceability, and tracking to ensure AI integrity by tracking prompts, vector databases, model versions, applied safeguards, and policy enforcement, providing full visibility into how AI systems operate and comply with security standards.
• Automated compliance enforcement to proactively address security gaps, reducing the need for last-minute audits and costly interventions, such as manual investigations or regulatory fines.

By consolidating all AI governance, observability and monitoring into one unified dashboard, leaders gain a single source of truth for real-time visibility into AI behavior, security vulnerabilities, and compliance risks—enabling them to prevent costly errors before they escalate.

Automated safeguards 

Automated safeguards, such as PII detection, toxicity filters, and anomaly detection, proactively catch risks before they become business liabilities.

With automation, AI leaders can:

• Free up high-value talent by eliminating repetitive manual checks, enabling teams to focus on strategic initiatives.
• Achieve consistent, real-time coverage for potential threats and compliance issues, minimizing human error in critical review processes.
• Scale AI fast and safely by ensuring that as models grow in complexity, risks are mitigated at speed.

Simplified audits

Strong AI governance simplifies audits through:

• End-to-end documentation of models, data usage, and security measures, creating a verifiable record for auditors, reducing manual effort and the risk of compliance violations.
• Built-in compliance tracking that minimizes the need for last-minute reviews.
• Clear audit trails that make regulatory reporting faster and easier.

Beyond cutting audit costs and minimizing compliance risks, you’ll gain the confidence to fully explore and leverage the transformative potential of AI.

Reduced tool sprawl

Uncontrolled AI tool adoption leads to overlapping capabilities, integration challenges, and unnecessary spending. 

A unified governance strategy helps by:

• Strengthening security coverage with end-to-end governance that applies consistent policies across AI systems, reducing blind spots and unmanaged risks.
• Eliminating redundant AI governance expenses by consolidating overlapping tools, lower licensing costs, and lowering maintenance overhead.
• Accelerating AI security response by centralizing monitoring and altering tools to enable faster threat detection and mitigation. 

Instead of juggling multiple tools for monitoring, observability, and compliance, organizations can manage everything through a single platform, improving efficiency and cost savings.

Secure AI isn’t a cost — it’s a competitive advantage

AI security isn’t just about protecting data; it’s about risk-proofing your business against reputational damage, compliance failures, and financial losses.

With the right governance and observability, AI leaders can:

• Confidently scale and implement new AI initiatives such as agentic flows without security gaps slowing or derailing progress.
• Elevate team efficiency by reducing manual oversight, consolidating tools, and avoiding costly security fixes.
• Strengthen AI’s revenue impact by ensuring systems are reliable, compliant, and driving measurable results.

For practical strategies on scaling AI securely and cost-effectively, watch our on-demand webinar.

About the author

Aslihan Buner

Senior Product Marketing Manager, AI Observability, DataRobot

Aslihan Buner is Senior Product Marketing Manager for AI Observability at DataRobot where she builds and executes go-to-market strategy for LLMOps and MLOps products. She partners with product management and development teams to identify key customer needs as strategically identifying and implementing messaging and positioning. Her passion is to target market gaps, address pain points in all verticals, and tie them to the solutions.