Agentic AI and the Pursuit of Transparency in Cybersecurity

As organizations scramble to fend off escalating digital threats, cybersecurity professionals are increasingly turning to agentic artificial intelligence—a new breed of AI systems designed to autonomously analyze, detect, and mitigate risks. Elastic’s Chief Information Security Officer, Mandy Andress, has emerged as one of the leading voices urging not only the wider adoption of these automated systems but also for a renewed focus on transparency in their decision-making processes. Her recent comments have prompted industry stakeholders and policy experts alike to ask: how do we trust a machine to defend the digital frontier while ensuring accountability and clarity in its actions?

In a landscape where every second counts, the promise of AI-driven solutions is compelling. Cybersecurity operations are witnessing a paradigm shift with AI agents capable of responding to threats in real time, exploiting vast data sets to pinpoint vulnerabilities, and even preempting attacks before they fully manifest. Yet, as organizations increasingly integrate these systems into their security frameworks, questions of oversight and accountability become ever more pressing. Can these agents be trusted with decisions that carry significant financial, operational, and even national security implications? And, crucially, how do we make these often opaque algorithms and decision trees understandable to human operators and regulators alike?

Historically, cybersecurity has always been a balance between proactive threat detection and comprehensive incident management. The advent of agentic AI is simply the next evolution in this long battle against digital adversaries. Conventional systems, though effective in many contexts, tend to react to threats with delays inherent to human analysis and administrative bottlenecks. Agentic AI promises to compress these delays by removing the need for human intervention in the midst of an unfolding crisis. However, with speed and efficiency come risks: the rapid pace of machine decision-making can obscure the logic behind an action and make it difficult to trace accountability when outcomes are adverse.

The call for enhanced transparency in AI decision-making is not without precedent. In sectors ranging from finance to healthcare, regulatory agencies have long emphasized the importance of explainability in automated decision systems. Within cybersecurity, where the stakes involve not just corporate data but national security, the need for clarity becomes even more critical. Analysts point out that AI agents, by design, operate on layers of complexity that make it challenging to identify the underlying rationale behind specific responses. Without a clear audit trail, decisions made by AI systems can appear arbitrary, undermining trust among users and regulators alike.

At a recent industry conference, Elastic CISO Mandy Andress articulated this tension vividly. “Deploying more AI agents for cybersecurity tasks is a double-edged sword,” Andress remarked. “While these systems can vastly improve our ability to detect and react to threats, they also introduce a level of opacity in decision-making that we cannot ignore. Trust in our security infrastructure depends on understanding the ‘why’ behind each automated action.” Her insights resonate widely in an era where both cyber and physical threats are increasingly interconnected, and where a failure in cybersecurity could lead to cascading failures across critical infrastructure.

Currently, some cybersecurity teams have begun to adopt hybrid models that integrate human oversight with agentic AI responses. According to public statements and technical briefings from industry leaders, these models aim to combine the speed of machine analysis with the nuanced judgment that only a human expert can provide. There is growing consensus that while full automation might be an attractive goal, the ideal system would incorporate “explainable AI” frameworks that allow operators to review how and why particular actions were taken during an incident. This integration of transparency is seen as essential to maintaining both operational security and public trust.

Transparency, in the context of agentic AI, encompasses several critical dimensions. First, it implies that the algorithms driving AI agents should be open to scrutiny by independent experts and regulatory agencies, ensuring that no hidden biases or vulnerabilities are built into the decision-making process. Second, there is a growing push for creating standardized audit trails that allow for post-incident reviews. Understanding the sequence of events leading to an AI-driven response is vital not only for refining those systems but also for addressing any legal or ethical concerns that may arise. Finally, transparency involves clear and accessible communication with stakeholders—the employees, investors, and even customers who rely on robust cybersecurity measures for their personal and professional security.

Several cybersecurity firms and regulatory bodies are already taking steps in this direction. Organizations like the National Institute of Standards and Technology (NIST) have published guidelines advocating for “explainable AI” in high-stakes environments like cybersecurity. These guidelines urge developers and operators to adopt methodologies that not only optimize performance but also document decision-making processes in a way that is accessible to human reviewers. In a similar vein, industry watchdogs have called on companies to incorporate transparency measures as a core component of their AI deployment strategies.

The implications of these developments extend beyond the technical realm. For policymakers, the rise of agentic AI in cybersecurity presents both an opportunity and a challenge. On the one hand, governments stand to benefit from systems that can rapidly address cyber threats before they escalate. On the other, without clear standards for algorithmic transparency, there is a risk of eroding public trust. The delicate balance between operational effectiveness and accountability is a subject of ongoing debate in legislative circles, where lawmakers are keenly aware that technology must serve the broader goal of safeguarding not only data but also democratic institutions.

Economic considerations also play a significant role. With cybercrime costing the global economy billions of dollars annually, enterprises are under pressure to adopt cutting-edge technology that promises to mitigate these losses. Yet, investors are equally wary of systems that lack robust oversight mechanisms. In capital markets, trust is currency. The more transparent these systems are in their operation, the more likely they are to garner sustained investment and support. Meanwhile, adversaries in the cyber domain are evolving their tactics, further underlining the need for systems that can both outperform threats and justify their actions in a public, scrutinizable manner.

Expert analysts note that achieving transparency in agentic AI is not merely a technical challenge—it is an interdisciplinary endeavor that requires collaboration among cybersecurity experts, legal scholars, and data scientists. For instance, integrating explainability into machine learning involves dissecting algorithmic decision paths without compromising on performance. This balance can often be difficult: increased transparency might expose potential vulnerabilities, while too little can lead to mistrust and regulatory backlash. In this context, experts like Dr. Ian Goodfellow of Apple and Yann LeCun of Meta have previously stressed the importance of designing AI systems that are both robust and interpretable—an ideal that is particularly relevant for cybersecurity applications.

Looking ahead, the conversation around agentic AI is likely to shift from mere adoption to governance and oversight. As organizations continue to deploy these AI agents, enhanced transparency will be key to ensuring that cybersecurity measures are both effective and accountable. Future policies may well require that all AI-driven security responses be accompanied by detailed logs and audit trails that can be reviewed by third parties. Moreover, the public sector might soon see increased regulatory frameworks designed to ensure that the benefits of AI do not come at the expense of oversight and fair practice.

For cybersecurity teams, the next few years will be a time of rapid evolution and adaptation. As systems grow more complex and threats more sophisticated, the need for transparent, explainable AI becomes a cornerstone of strategic planning. Whether through mandated industry standards or voluntary best practices, the movement toward transparency promises to bolster security while building trust among stakeholders. It is a reminder that behind every line of code and every automated response are professionals striving to protect digital ecosystems in an age where information is gold, and trust is paramount.

Ultimately, the debate over agentic AI in cybersecurity is emblematic of a broader challenge facing modern technology—a challenge that pits innovation against the imperative for accountability. As organizations like Elastic push for systems that not only defend but also explain, the conversation serves as a call to action for technologists, policymakers, and industry leaders alike. Can the digital defenders of tomorrow afford to sacrifice transparency in the name of speed and efficiency, or is an open, accountable approach the only viable path forward in an increasingly interconnected world? The answer, it seems, will determine the future of cybersecurity as much as any binary code ever could.