Your AI strategy has a blind spot, and you don’t see it, but your attackers do.
AI is now running real operations at most companies. The race to deploy is moving faster than the work to secure. Organizations across industries are racing to deploy AI for its strategic value, pursuing faster insights, lower operating costs, and new revenue streams. But in that race, a dangerous gap is widening. Most organizations are deploying AI far faster than they're securing it, building on foundations that lack the resilience to manage the risks AI introduces. The result is a growing exposure to traditional cyber threats and an entirely new class of AI-enabled attacks targeting the very models, data pipelines, and automated decisions these organizations now depend on.
Security ambitions and AI ambitions are on a collision course. Enterprises are investing heavily in AI capabilities while underinvesting in the threat maturity, data security practices, and governance structures needed to protect them. AI without resilience baked in doesn't create lasting value. It creates liability.
The threat landscape is growing more complex by the day. Deepfakes are becoming indistinguishable from reality, AI-powered fraud can mimic trusted voices and identities with alarming precision, and AI-generated content is flooding every channel, often making it harder to tell what is real and what is not. Attackers are using the same AI capabilities organizations rely on for growth. In this environment, security and operational readiness are not ethical nice-to-haves or compliance checkboxes. They are necessities that determine whether an organization can detect, withstand, and recover from AI-driven disruption.
The companies handling this well treat security requirements, governance, and recovery planning as design constraints, not features. Bolted-on security at the end of development is not security. It's a press release. These disciplines must be fundamental to how AI systems are designed, deployed, monitored, and governed, and woven into the culture, architecture, and decision-making fabric of the organization. Those that build them in at every stage will protect operations, accelerate responsible innovation, and earn stakeholder confidence. Those that don’t find that the same AI powering their ambitions is also their greatest vulnerability.
Two Problems, One Conversation: Securing AI and Defending With It
As AI is embedded into essential business operations, organizations must think about AI security in two distinct but interconnected dimensions:
Securing AI | Protecting the Models and the Automation that uses them
AI systems introduce entirely new attack surfaces that traditional cybersecurity controls were never designed to address. Threats like model poisoning and prompt injection require fundamentally new approaches to detection and monitoring.
Organizations deploying AI must build runtime defenses that go beyond perimeter security and address threats at the model, data, and system layers.
But even when the model itself is locked down, the ecosystem around it may not be.
Consider Anthropic's Claude Mythos Preview, an advanced AI model capable of surpassing the most skilled humans at finding and exploiting software vulnerabilities. Anthropic restricted access to roughly 40 elite organizations through its Project Glasswing initiative. Yet within 24 hours of its announcement, unauthorized users gained access, not by breaching Anthropic's core infrastructure, but through a third-party vendor environment.
Securing AI means securing the model and its entire supply chain. Every model provider, training data source, third-party evaluator, and API dependency is an attack surface. The most capable AI model in the world is only as secure as its weakest vendor link.
AI agents add risk in a different way than traditional applications. They can be granted credentials, access data, call tools, and take actions across systems, often with far less visibility than a human user and with permissions that persist long after the original project ends. When agents are created quickly through scripts, plugins, or SaaS automation, they can spread quietly across the environment and become difficult to inventory, monitor, and retire.
A recent Cloud Security Alliance survey underscores how quickly that attack surface is expanding: 82% of enterprises report they have unknown AI agents operating in their environments, and nearly two in three (65%) report AI agent-related incidents in the past year, with impacts ranging from data exposure (61%) and operational disruption (43%) to financial losses (35%).1
AI for Security | Defending Against AI-Powered Attacks
On the flip side, adversaries are using AI to launch increasingly sophisticated attacks, hyper-personalized social engineering, AI-generated phishing at scale, and deepfake-driven fraud. The same capabilities that help defenders move faster can also help attackers scale.
AI can also strengthen defense when it is deployed with the right controls. Used well, it can improve organizational resilience across three critical phases:
- Prepare: AI identifies and predicts potential disruptions before they materialize, modeling threat scenarios, surfacing hidden risks, and improving readiness against future attacks.
- Guard: AI detects anomalies faster than any human team, reducing dwell time from several days to near real-time identification and containment.
- Respond: AI accelerates immediate incident response, automating containment workflows, correlating threat intelligence, and orchestrating remediation at machine speed.
A model like Mythos illustrates the tension. Its ability to uncover deep-layered vulnerabilities at unprecedented speed represents a leap forward in defensive capability, yet its compromise through a supply chain gap shows how quickly that same power can be exposed. The tools that defend us must themselves be defended.
AI can’t replace human judgment in cybersecurity. It amplifies its need. And organizations that harness AI to aid human defenses will be far better positioned to weather the threats ahead.
Building True AI Resilience: Beyond Governance and Prevention
Governance frameworks and preventive controls are necessary but not sufficient. To demonstrate genuine cybersecurity resilience for AI risks, organizations must go further:
- Anticipate threats and failure modes. AI models and the emerging agents that use them add a layer of complexity and tenacity that exceeds much human imagination. Focusing on what can go wrong and how it will impact operations or critical data loss will change everything. We need to be prepared for the unexpected.
- Establish AI-specific governance and oversight structures. Traditional IT governance wasn't designed for speed, opacity, or the risk profile of AI systems. Organizations need dedicated AI governance frameworks that define accountability for model risk, enforce ethical use policies, mandate transparency in AI decision-making, and align AI initiatives with enterprise risk appetite. Without clear ownership and oversight, even the best technical controls lack the authority to enforce them.
In practice, visibility is often overestimated. In the same CSA survey, 68% of organizations said they have strong visibility into AI agents, yet 82% reported discovering previously unknown agents in the past year. This is evidence of a confidence gap that governance must close.2
- Invest in AI runtime defenses. Don't stop at access controls and policies. Deploy monitoring, anomaly detection, and guardrails that operate during model inference, catching prompt injections, data exfiltration, and adversarial manipulation in real time. Static pre-deployment testing alone cannot account for how models behave under live, adversarial conditions.
- Integrate supply chain risk into core cyber risk management. Map your AI supply chain, including every model provider, training data source, third-party evaluator, and API dependency. Treat these as critical attack surfaces, not trusted partners by default. A single compromised vendor or poisoned dataset can undermine an entire AI ecosystem.
- Develop AI-specific incident response plans and playbooks. Traditional IR playbooks weren't built for model compromise, training data poisoning, or adversarial prompt exploitation. Organizations need dedicated response procedures that address the unique characteristics of AI incidents, including model rollback, data provenance investigation, and coordinated disclosure with AI vendors.
Lifecycle discipline matters here too. The CSA survey found that only 21% of organizations have formal AI agent decommissioning processes, meaning agents can linger past their intended use, retaining access and credentials that expand the blast radius when something goes wrong.3
- Go beyond prevention and demonstrate resilience. The question isn't "will we be targeted?" it's "how fast can we detect, contain, and recover?" Many security leaders are already shifting focus toward resilient, adaptive programs that assume breach and prioritize recovery over perfection.
AI can be the most powerful tool an organization deploys, or its greatest vulnerability. The difference lies in whether security, accountability, and recovery capabilities are treated as core properties of the system, not optional features layered on after deployment.
The organizations that will thrive in an AI-driven world are those that recognize a simple truth: AI adoption without the ability to manage risk, respond to incidents, and recover quickly is just risk adoption.
Ready to move from intent to execution? Connect with our cybersecurity team to assess your AI risk exposure, strengthen governance and runtime defenses, and build an incident response approach designed for AI-driven threats.
References
1,2,3 (New Cloud Security Alliance Survey Reveals 82% of Enterprises Have Unknown AI Agents in Their Environments, 2026)