Most of the conversation around AI in cybersecurity focuses on how attacks are getting faster and more sophisticated. That is true, but it misses a more immediate issue. Many security teams are still operating in ways that assume a much slower threat environment.
AI-enabled attacks are already reshaping how organizations get compromised, and the impact is showing up quickly. AI-enabled scams alone drove $16.6 billion in reported losses in the United States in 2024. That number is a signal of how fast things are moving and how effective these tactics are becoming. AI is only accelerating how quickly these attacks are launched, tested, and refined.
On the flip side, detection, triage, and response processes are still often built around human-paced workflows, static playbooks, and fragmented tools. That mismatch is where teams are starting to feel the strain.
At the core, the challenge has less to do with tools and more to do with how security teams are set up to operate day to day.
AI is a force multiplier for attackers
AI is lowering the barrier to entry while increasing attacker productivity. With AI, it’s much easier to produce highly tailored phishing campaigns at scale. Voice cloning and deepfakes make impersonation more believable. Malware can evolve to avoid detection. Automated scripts can probe environments without pause.
The result means faster cycles of iteration. Attackers can test, adjust, and try again in near real time.
We are already seeing this play out. In one case, an attacker with limited technical expertise used AI-enabled techniques to compromise hundreds of firewalls and move across multiple environments. That kind of scale used to require far more sophistication.
At this point, it is safest to assume AI is part of the attacker toolkit by default.
More tools are making the problem harder, not easier
The natural response has been to invest in more AI-powered security tools. These tools can help, but in many cases, they are adding complexity faster than reducing risk.
Tool sprawl leads to more alerts, less clarity, and unclear ownership. Teams end up spending more time sorting through noise and trying to connect signals than actually responding to threats.
In a slower non-AI-enabled threat environment, that was manageable. In today’s world, it is not. The bigger issue is how those tools fit into existing workflows, and whether those workflows can keep up with the pace of threats.
For many teams, this shows up as a constant backlog of alerts, slower response times, and growing uncertainty about what actually matters. The issue is confidence in decision-making at speed.
Security operations need to move faster
Traditional detection and response models rely on escalation paths, manual triage, and predefined playbooks. All of that assumes there is time to investigate and decide before acting. AI-enabled attacks remove a lot of that time.
Security teams are starting to shift toward more continuous monitoring, automated triage, and more adaptive response approaches. Instead of reviewing alerts one at a time, they are using automation to enrich signals, prioritize what matters, and trigger, or in some cases fully take, initial actions in real time.
This does not replace human judgment. It changes where it is applied. Less time on repetitive triage, more time on decisions that actually require context.
The goal is simple. Move faster without losing control.
AI literacy cannot sit only within security
There is also an organizational gap. In many companies, AI knowledge sits within a small group, usually in security or data teams. Everyone else is expected to operate around it. That approach is starting to break down.
AI risk shows up in products, in workflows, and in third-party tools. Security teams cannot manage that alone. Engineering teams need to understand how these threats interact with the systems they build. Business leaders need to understand the operational and customer impact.
AI literacy is no longer confined to security teams. The risk is already embedded in how products are built and how the business operates.
This requires a shift in how teams operate
AI is putting pressure on how security teams are structured today.
Adding tools without better enabling teams and enhancing processes creates more noise. Relying on manual processes slows response. Keeping AI knowledge isolated creates blind spots.
The organizations making progress are approaching this differently. They are focusing on integration over accumulation, using automation where it actually reduces friction, and clarifying ownership across teams.
That means:
- Prioritizing integrated visibility over adding more point solutions
- Using automation to handle triage and speed up early response
- Designing processes for continuous activity, not periodic review
- Treating AI risk as a shared responsibility across the business
The bottom line
For cybersecurity teams and leaders, this comes down to rethinking how the security program actually operates. It goes beyond adding new capabilities. Security teams need to rethink how processes are designed, who owns decisions, and how quickly they can respond, including using AI proactively.
The organizations doing this well are improving how they respond and building more resilience into their operations. That will matter more as AI becomes part of how the business runs as well as how attackers continue to leverage it against companies.
