Cyberattacks on Steroids: Why Organizational Resilience Is Becoming a Critical Line of Defense in the AI Era
AI is already at the center of the cybersecurity risk landscape, and the rise of AI agents is raising the stakes even further. Prevention remains the first line of defense, but what happens when an attack gets through? This is where organizational resilience comes into play: the ability to respond, limit the damage, recover, and keep critical business processes running.
By Amir Atzmon, VP of Consulting, 2Bsecure
When we talk about AI in cybersecurity, much of the conversation focuses on attackers: how they can create more convincing phishing campaigns, write malicious code faster, and launch attacks at greater speed and scale. All of this matters, but it is only part of the picture.
One of the most significant changes AI brings is the pace of events, or more specifically, the speed and scale at which actions can be carried out across the attack chain. As a result, an organization’s ability to detect an incident, make decisions, and respond quickly is itself becoming part of its defense.
This shift requires organizations to rethink not only cyber resilience, but organizational resilience as a whole. The question is no longer simply whether we can prevent the next breach. It is also how long it will take us to realize something has happened. Will we be able to make the right decisions in time? How quickly can we contain the incident, limit its impact, recover, and keep operating?
In other words, defense is an essential part of organizational resilience, but it is not the only part. Resilience is also measured by an organization’s ability to respond to an incident, minimize its impact, recover, and keep critical business processes running.
As threats accelerate, the organization becomes the bottleneck
This year, the World Economic Forum, in collaboration with Accenture, published its Global Cybersecurity Outlook 2026. The findings show just how central AI has become to the cybersecurity risk landscape. Ninety-four percent of respondents expect AI to be the most significant driver of change in cybersecurity over the coming year, while 87% identified AI-related vulnerabilities as the fastest-growing cyber risk during 2025.
AI is having an impact on both sides of the equation. It enables attackers to increase the speed, scale, and sophistication of their operations, while also helping defenders accelerate detection and response and analyze large volumes of information.
In this environment, an organization’s response time becomes part of its defensive capability. An organization can deploy advanced detection systems and use AI to analyze vast amounts of data and alerts. But if isolating a system, blocking an identity, or stopping a process still requires a long chain of approvals, technology may no longer be what is slowing the response. The bottleneck may be the organization itself.
A new player enters the equation: the AI agent
AI agents add a new dimension to the challenge. Unlike a model that generates content or analyzes information, an agent can be given access to data and tools, connect to systems, and take actions autonomously or semi-autonomously. As its capabilities and permissions expand, so does the potential impact of an error, misuse, or manipulation.
One example is Agent Hijacking, where an attacker embeds malicious instructions in information processed by an agent, such as an email, document, or website. Those instructions can cause the agent to deviate from its intended task and perform actions it was never meant to take.
Research published this year by CAISI, the U.S. Center for AI Standards and Innovation, illustrates the challenge. In a large-scale red teaming competition, 13 frontier models were tested in more than 250,000 attack attempts by over 400 participants. The scenarios included tool-use agents, coding agents, and computer-use agents. At least one successful hijacking attack was found against every model tested.
The implications go beyond protecting the AI agent itself. When an agent is connected to enterprise systems and operates with legitimate permissions, manipulating its behavior can turn it into an attack path to the data, tools, and systems it can access. An attacker who succeeds in getting an agent to act against its intended purpose may be able to exploit the agent’s permissions to access information or take actions within enterprise systems. From a cyber resilience perspective, the question is therefore not only how to prevent Agent Hijacking, but also how to detect, contain, and stop such an incident before it spreads to other systems and processes across the organization.
The introduction of agents into the equation has another implication for organizational resilience. It is not simply that organizations now face a new attack vector. They are also introducing components capable of operating at machine speed, while organizational processes for detection, approval, and decision-making may still operate at human speed. That gap is precisely where organizational resilience is put to the test.
What should organizations be looking at today to strengthen cyber resilience?
Use AI on the defensive side as well. AI can help prioritize alerts, identify anomalies, analyze large volumes of information, connect signals across different sources, and execute predefined playbooks. The goal is not to remove people from the decision-making process, but to use automation where it can shorten response times while preserving human judgment where it matters.
Shorten the path from detection to decision, and from decision to action. During a cyber incident, there is no time to start figuring out who is authorized to do what. Authority, responsibilities, escalation procedures, and response scenarios should be clearly defined and rehearsed in advance. Organizations should also determine which actions can be automated, which require human approval, and who has the authority to provide that approval in real time.
Exercise the new scenarios. Management and technical teams should practice incidents that unfold rapidly and involve large volumes of information, sometimes contradictory, AI-generated content that appears authentic, or an agent behaving in unexpected ways. It is not enough to test whether the SOC detected the incident. Organizations need to know whether the business as a whole can understand what is happening, make decisions, and respond under pressure and uncertainty.
Measure resilience, not just prevention. In addition to tracking blocked incidents and remediated vulnerabilities, organizations should measure detection time, decision time, containment and recovery time, their ability to maintain critical business services, and the quality of collaboration across teams.
Perhaps it is time to add another metric alongside resilience measures such as MTTD and MTTR: Mean Time to Decision. The question is not only how quickly an incident was detected or how quickly the organization responded, but how long it took to make the decision that enabled action. As technology accelerates detection and response, decision time may become a critical measure of organizational resilience.
Make AI governance part of the resilience framework. AI use within organizations is no longer limited to technology teams. Organizations therefore need to define in advance which tools are approved, what information may be shared with them, how AI-generated outputs should be validated, what permissions AI agents can receive, and who is responsible for managing the associated risks. As AI systems gain greater autonomy and the ability to take action, appropriate monitoring, control, and stop mechanisms become increasingly important.
Build resilience into AI-dependent processes as well. As more business processes come to rely on models, agents, and AI services, organizations need to ask what happens when those systems become unavailable, produce incorrect results, or behave unpredictably. Just as organizations plan for the business continuity of other critical systems and infrastructure, they should map their dependencies on AI and establish alternatives and recovery procedures in advance.
AI does not change the fundamental principle: prevention remains the first line of defense. But as threats become faster and more automated, while organizations simultaneously introduce AI systems with permissions and the ability to take action, prevention alone is not enough.
Ultimately, AI is accelerating both sides of the equation. It allows attackers to operate faster and at greater scale, but it also gives organizations tools to detect, analyze, and respond more quickly. In this environment, cyber resilience will not be measured solely by the defensive technologies an organization deploys. It will also be measured by its ability to shorten the time between detection, decision, and response, to adapt and learn quickly, and to keep critical business processes running when something goes wrong.
