Agentic AI is quickly emerging as the next major disruption in the tech industry, moving AI from just chatbots into autonomous decision makers. Unlike traditional AI tools that require constant prompting, agentic AI operates with a degree of independence, learning, reasoning and acting in pursuit of specific goals.
In fact, by 2028, one-third of enterprise applications will include agentic AI, up from less than 1% in 2024, with up to 15% of routine workplace decisions made autonomously. For enterprise leaders, this represents a huge change in how technology supports and shapes the business, particularly in the field of cybersecurity.
Co-Founder and Senior Director of Product Management at HackerOne.
Indeed, Agentic AI has the capacity to transform teams’ ability to escalate the most critical risks at intake, ensure higher-quality submissions, and filter out duplicates so teams can focus on what matters. Yet the autonomy that makes it powerful also introduces new risks for security teams.
Enterprise leaders need to understand both how Agentic AI can strengthen their defenses and what pitfalls to watch for when deploying it.
What makes Agentic AI different?
Agentic AI is built around autonomous agents, with systems able to reason, adapt and take independent action. It’s a departure from both conventional automation and earlier forms of AI. Traditional machine learning models largely produce outputs based on prompts or fixed parameters.
Agentic AI, by contrast, can operate iteratively, evaluate context, plan a course of action, adapt when conditions change and improve through experience.
The cybersecurity industry is moving away from the past where bots just flag suspicious logins and towards a connected system that autonomously investigates, escalates priority vulnerabilities, and provides actionable insights to the user.
Enhancing cybersecurity capabilities
Agentic AI is well suited to some of the most pressing challenges in security:
Threat detection and response: Security operations centers (SOCs) are often inundated with alerts, many of them false positives. Agentic AI can autonomously investigate routine alerts, escalating only those that require human judgment.
This reduces “alert fatigue” and allows analysts to focus on high-priority incidents. The impact can lower the time to detect issues, shortening the window attackers have to exploit vulnerabilities, while reducing the time to remediation.
Penetration testing: Agentic AI can accelerate vulnerability discovery by scanning attack surfaces and finding common issues at scale. Human testers are then freed to focus on the creative, high-impact aspects of testing that machines cannot replicate. The result is broader coverage and more frequent, cost-effective testing.
Vulnerability management and validation: Noise in vulnerability management is at an all-time high, frustrating in-house security teams. Prioritizing which vulnerabilities to remediate by validating what is real is a complex task. This requires historical context, business impact analysis and technical expertise.
Agentic AI can perform much of the groundwork, such as standardizing reports, comparing with past incidents and recommending actions, while keeping humans in the loop to prioritize business impact in final decisions.
Scalability: Recruiting and retaining skilled analysts is difficult and expensive. By automating large parts of security workflows, Agentic AI can chain tools together and adapt to feedback. This enables organizations to limit cost increases, and keep staff focused on strategic priorities that require human ingenuity.
Of course, using agentic AI to strengthen cybersecurity is only half the story. The security of agentic AI itself must also be treated as a priority. Otherwise, the very systems designed to protect the enterprise could become new attack vectors.
The risks enterprise leaders must manage
While autonomy brings advantages, it also requires careful oversight. Left unchecked, agentic AI can misjudge, misfire or be manipulated. Therefore, it’s important to pay close attention to the following areas:
Prompt injection: As AI agents interact with external data sources, attackers can embed malicious instructions designed to steer outcomes. A prompt injection that seems trivial in a chatbot can cause far greater damage when an autonomous agent is making security decisions.
Therefore, it’s essential to maintain continuous monitoring and implement robust guardrails.
Data access and privacy: AI systems excel at processing large datasets, which creates risk if access controls are weak. As a result, sensitive information buried in overlooked repositories can be inadvertently exposed. Organizations need to have strong data governance and strict control of training and operational datasets.
Jailbreaking: Even with guardrails, threat actors may attempt to “jailbreak” an AI system, convincing it to ignore restrictions and act outside its intended scope. Combined with prompt injection, this could lead to severe outcomes, such as unauthorized financial transfers.
To reduce these risks, organizations should implement ongoing red teaming to stress test AI systems.
Embracing agentic AI
As AI adoption is expected to grow at an annual rate of 36.6% between 2023 and 2030, this is both an opportunity and a challenge. If enterprises do not embrace agentic AI, the asymmetry between attackers and defenders will widen, particularly given the ongoing skills shortage in cybersecurity.
With it, security teams can multiply their capacity, reduce time-to-response and move from reactive firefighting to continuous threat management.
To achieve balance, agentic AI should be deployed with clear governance frameworks, human oversight at critical stages and a strong focus on data security. Collaboration between developers, security professionals and policymakers will be central to ensuring these systems serve the interests of organizations and wider society.
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