As parts of the software market cool, security vendors are benefiting from a new wave of demand tied to artificial intelligence. Companies deploying AI agents are generating fresh network traffic and novel attack paths, pushing cybersecurity platforms to the center of enterprise spending plans.
The shift is visible in board-level discussions and budget choices across major industries. AI pilots that started in labs are moving into customer support, coding assistance, and back-office automation. Each step adds machine-to-machine activity and sensitive data flows that must be verified, logged, and controlled.
“While the broader software sector stumbles, cybersecurity platforms are emerging as essential infrastructure needed to channel rising traffic from AI agents.”
Why AI Changes Security Needs
AI agents act on behalf of users and services. They call APIs, read documents, write code, and trigger workflows. That creates many small, fast interactions that are different from traditional user sessions. Security teams now need to verify not only people, but also automated actors that can chain tasks without a person watching every step.
Identity has become the first control point. If an agent cannot be tied to a clear identity with scoped permissions, it becomes a risk. API gateways, identity providers, and secrets managers are being reconfigured to assign and rotate credentials for non-human accounts at scale.
Network monitoring is also shifting. Instead of focusing mainly on north-south traffic, teams are watching east-west calls between services. The goal is to spot unusual agent behavior, such as mass file access, unexpected code execution, or data movement across regions.
From Point Tools to Platforms
Enterprises tested many point tools during early AI trials. Now, they are consolidating on platforms that tie identity, data protection, and runtime defense together. Vendors that can map data lineage, enforce policy at the API layer, and provide audit trails are gaining ground.
Security leaders describe three priorities for the next year:
- Strong identities for non-human accounts, with least-privilege access and short-lived tokens.
- Data loss prevention tailored to prompts and outputs, including redaction and filtering.
- Runtime controls for agents, such as egress rules, rate limits, and anomaly detection.
Zero trust principles are being applied to agents just as they were to users. Each request needs continuous verification, not a one-time login. That model is fueling interest in secure access services and microsegmentation in hybrid clouds.
The Spending Picture and Market Impact
Analysts report that security and risk budgets are still growing at a double-digit pace, even as other software categories slow. Board audit committees are more willing to fund controls that reduce AI-related exposure than to expand discretionary application suites.
Industry buyers say they are looking for fewer vendors that can cover more ground. That favors providers with end-to-end offerings across identity, endpoint, cloud workload protection, and API security. Startups with narrow features may be acquired or may partner to reach large accounts.
Public agencies are also shaping demand. Draft rules in the United States and Europe call for safeguards around high-risk AI systems. Many of those controls—access management, logging, incident reporting—are standard security practices that now apply to AI deployments as well.
Operational Challenges for Security Teams
Even with new tools, staffing remains tight. Security teams must write policies that machines can follow without blocking real work. They also need visibility into prompts, tools an agent can use, and the data each workflow touches.
Chief information security officers warn that over-permissive agents can escalate small misconfigurations into major events. A single leaked API key or unmonitored connector may let an agent exfiltrate large volumes of data in minutes.
Vendors are responding with “guardrail” features. These include allowlists for tools, output filters, and step-by-step approvals for sensitive actions. Some companies are piloting model gateways that standardize logging and policy enforcement across multiple AI providers.
What To Watch Next
Several questions will shape the next phase:
- Can enterprises measure the return on security investments tied to AI risk reduction?
- Will platforms deliver usable, unified policy for human and non-human identities?
- How quickly will regulators finalize audit and reporting expectations for AI systems?
For now, buyers appear to be rewarding vendors that can make AI rollouts safer without heavy friction for developers. Clear mappings between controls and business risk are helping deals close even as broader IT spending softens.
The message from the market is consistent with the sentiment voiced by industry participants: security is becoming the gatekeeper for AI at scale. As agent traffic grows, the companies that can authenticate every actor, govern every call, and track every data touchpoint are set to lead. The next test will be delivering these controls at cloud speed, without slowing the promise of automation.
