In the past several years, AI has increasingly been adopted by individuals and organizations alike for both personal and business purposes. Unfortunately, it has also been implemented by many cybercriminals to enhance their attacks, and AI-powered threats are already impacting most organizations. There is a widening gap between AI adoption and security maturity, introducing significant risk to many organizations. Darktrace’s The State of AI Cybersecurity 2026 report shows that the risk of AI usage is not a theoretical future concern, but a current operational danger.
From Tools to Teammates: The Rise of Agentic AI
One of the more recent trends in the AI explosion is the growing popularity of agentic AI tools, which tend to have far-reaching access to systems and permissions to carry out a wide range of actions. The implementation of AI agents can pose serious risks, as these tools function like digital workers with system access. Organizations often have less visibility, monitoring, and control of AI agents than they do over human employees.
The autonomous capabilities of agentic AI introduce new risk dynamics that traditional security measures struggle to handle. AI agents often have more access than any given individual, but they lack human abilities of judgment, ethics, and accountability, increasing the exploitability of these tools. These tools cannot interpret their own actions as potentially malicious, leading them to make security mistakes that human workers are trained to avoid.
Why Security Teams Feel Unprepared
According to the Darktrace report, 92% of security professionals are concerned about AI agents and their impact on security. Organizations’ investment in AI tools is outpacing the evolution of governance and oversight, creating a growing gap between technology and security. The limited visibility into AI models, tools, and agents makes it difficult to effectively secure them against threats, especially as they introduce new and evolving risks.
Traditional security controls, designed for older systems and problems, fail to map cleanly onto AI workflows. AI tools and agents operate differently from human workers or traditional technologies, requiring a shift in security approaches and capabilities to properly secure them.
Data Exposure at Machine Speed
The speed of automated and agentic tools is a major factor in the risk these tools introduce to organizations. Over three-fourths (77%) of security stacks now employ generative AI tools in some way, aiding in many processes and business operations, but simultaneously introducing significant risk.
AI systems ingest and act on sensitive enterprise data, requiring significant access and permissions to carry out their functions, and they do it all at machine speed. Any misconfigurations and excessive permissions scale rapidly to create massive security gaps. Breaches can occur through AI tools and agents without ever triggering conventional tools and alerting security teams.
Shadow AI and Invisible Risk
Shadow IT is a longstanding problem in cybersecurity, with many organizations lacking full control over their technology stacks. Sprawling and complex environments employ tools without comprehensive visibility and monitoring of their actions, or even their presence in the system. These under-monitored tools often serve as ideal vectors for initial access and malicious activity in cyberattacks.
The addition of AI capabilities significantly amplifies the dangers associated with shadow IT, as the proliferation of unsanctioned AI tools and agents is on the rise. Data flows to external AI services are often untracked, hindering the functionality of monitoring and security tools and creating gaps in security strategies. Use of approved AI platforms can be inconsistent or risky.
Behavior, Not Signatures, Defines AI Security
Traditional security measures often fail to detect threats in dynamic AI environments. Tools and measures based in known threat signatures and static rules are not effective in identifying the kind of harmful activity that tends to arise from the use of AI tools and agents. In AI-enhanced systems, a wide range of malicious activity is possible without alerting security tools that are designed to detect things like malware signatures and suspicious file types.
In order to protect against threats in the age of AI, it is important to utilize modern security techniques. Of the security leaders surveyed by Darktrace, 96% say that the use of AI in defenses significantly improves their security capabilities. Behavioral analysis can enable the detection of abnormal intent and drift, which is key to prevention. Establishing a baseline of normal activity with behavioral monitoring is essential for detecting anomalous behavior that could indicate threats in these systems.
Securing AI Means Securing Identity, Access, and Intent
AI systems operate as non-human identities, which behave differently from human users. Traditional identity and access management (IAM) strategies focus on human activity and fail to account for the risks introduced by AI tools and agents. Permissions, access paths, and interactions must be mapped in complex environments. Risk emerges when AI exceeds intended authority, which can easily happen within systems where AI is not adequately secured and monitored.
Protecting against the range of AI-related threats demands advanced security measures designed for the modern age. “As attackers use AI to automate attacks, they move faster in gaining access and spreading inside the network; defenses built for human response times fail silently,” says Ram Varadarajan, Chief Executive Officer at Acalvio. “CISOs investing in AI-native security aren’t chasing efficiency. They’re closing a fundamental speed gap between attack and defense.”
Preparing for Secure AI Adoption
Ensuring security is crucial for organizations implementing new and evolving AI tools. In the current landscape and moving forward, AI security must be a foundational aspect of any strategy, not merely reactive. Organizations require continuous visibility and control over all systems and tools, including those powered by AI. Early investment in governance is the main factor that determines whether AI will be able to scale safely in an organization.
