The Hidden Costs of AI: Securing Inference in an Age of Attacks

AI’s potential is undeniable, but the hidden security costs at the inference layer are a major concern for enterprises. New attacks targeting AI’s operational side are causing budget inflations, regulatory compliance issues, and erosion of customer trust. These factors are threatening the ROI and TCO of enterprise AI deployments.

Enterprises are excited about the transformative insights and efficiency gains that AI promises. However, as they rush to implement their AI models, they are encountering a harsh reality: the inference stage, where AI translates investment into real-time business value, is under attack. This critical stage is driving up the total cost of ownership (TCO) in ways that were not initially anticipated in the business cases.

Security executives and CFOs who approved AI projects for their potential benefits are now facing the unexpected expenses of defending these systems. Adversaries have identified inference as a vulnerable point in AI systems and are exploiting it to cause damage. The result is a rise in costs related to breach containment, compliance retrofits, and trust failures. Without effective cost control at the inference stage, AI implementations can become budget wildcards.

AI inference is rapidly becoming a significant risk factor, as highlighted by technology leaders at events like RSAC 2025. There is a common blind spot in enterprise strategies when it comes to securing AI inference. Organizations often focus on securing the infrastructure around AI while neglecting the inference stage. This oversight leads to underestimated costs for continuous monitoring systems, real-time threat analysis, and rapid patching mechanisms.

Another critical issue highlighted by industry experts is the assumption that third-party AI models are inherently safe to deploy. In reality, these models may not have been evaluated against an organization’s specific threat landscape or compliance requirements. This can lead to harmful or non-compliant outputs that erode brand trust. Inference-time vulnerabilities, such as prompt injection, output manipulation, or context leakage, can be exploited by attackers to produce damaging outcomes, especially in regulated industries.

When inference is compromised, the consequences impact multiple aspects of TCO. Cybersecurity budgets escalate, regulatory compliance is at risk, and customer trust diminishes. According to the State of AI in Cybersecurity survey by CrowdStrike, only 39% of respondents believe that the rewards of generative AI outweigh the risks, while 40% see them as comparable. Safety and privacy controls have become top priorities for new AI initiatives, with organizations focusing on mitigating risks such as sensitive data exposure and adversarial attacks.

The unique attack surface exposed by AI models is being exploited by attackers through various methods like prompt injection, insecure output handling, training data poisoning, and model denial of service. To defend against these threats, organizations must treat every input as a potential hostile attack and implement frameworks like the OWASP Top 10 for Large Language Model Applications.

Foundational security measures are essential for securing AI systems in this new era. Security experts emphasize the importance of enforcing unified protection across all attack paths, implementing rigorous data governance, robust cloud security posture management, and identity-first security through cloud infrastructure entitlement management. Identity should be considered the new perimeter, and AI systems must be governed with strict access controls and runtime protections.

The specter of “shadow AI” poses significant risks to enterprises, as unsanctioned use of AI tools by employees can create unknown vulnerabilities. Addressing this challenge requires clear policies, employee education, and technical controls like AI security posture management to discover and assess all AI assets, whether sanctioned or not.

To fortify the future and defend against AI attacks, organizations must adopt actionable defense strategies. Adversaries may have weaponized AI, but defenders are leveraging AI for cybersecurity purposes to analyze vast amounts of data and enhance their security posture. Budgeting for inference security from day zero, implementing runtime monitoring and validation, and adopting a zero-trust framework for AI environments are key strategies for protecting AI ROI.

Protecting the ROI of enterprise AI requires collaboration between CISOs and CFOs to model the financial benefits of security investments. By linking cybersecurity spending to TCO reductions and avoided costs, organizations can build a defensible ROI argument and protect their AI investments, budgets, and brands.

In conclusion, safeguarding AI inference is crucial for the financial sustainability of AI projects. Organizations must balance investments in AI innovation with investments in AI protection to ensure long-term growth and success. Strategic alignment between CISOs and CFOs is essential to manage the true cost of AI and turn it into a sustainable, high-ROI engine of growth.