Laurent Giraid is a distinguished technologist with a profound focus on the intersection of Artificial Intelligence and cybersecurity. With an extensive background in machine learning and natural language processing, he has dedicated his career to exploring how agentic systems can be ethically and effectively integrated into enterprise environments. As organizations face increasingly sophisticated threats from adversaries using automated tools, Giraid’s insights into the shift from reactive chatbots to skill-based autonomous agents provide a vital roadmap for the future of digital defense.
The following discussion explores the evolution of AI in the security operations center, moving beyond the limitations of prompt engineering to a model of pre-programmed expertise. We delve into how role-based automation can alleviate the burden on security teams, the importance of deterministic foundations in risk assessment, and the necessity of maintaining human oversight in an increasingly automated world.
Traditional AI assistants often require complex prompt engineering to yield useful results. How do pre-programmed security skills change the daily workload for a security analyst, and what specific guardrails are necessary to ensure these autonomous workflows remain accurate without constant manual tuning?
The shift from prompt-based chatbots to pre-programmed security skills is a fundamental change in how we perceive the “user experience” of security. Instead of an analyst spending thirty minutes trying to figure out the right syntax to ask a question, the system already understands the 25 years of security workflows we’ve encoded into its DNA. For the analyst, this means their daily workload moves from data entry and query construction to actual decision-making based on high-fidelity recommendations. We implement strict guardrails by using a closed-loop system within the customer’s environment, ensuring that the AI doesn’t hallucinate or pull in unverified external data. By grounding these skills in the Forescout 4D Platform telemetry, the system remains accurate because it’s analyzing real-time asset intelligence and policy, not just making statistical guesses.
Security teams frequently struggle with fragmented alerts and the exhausting process of chasing signals across multiple disconnected tools. How does providing a personalized, role-based view of overnight environment shifts improve remediation speed, and what specific metrics should organizations track to measure a successful transition from defensive to offensive posture?
When an analyst logs in, they shouldn’t be greeted by a list of 10,000 disconnected alerts; they should see a narrative of what shifted in their environment while they were asleep. By providing a personalized view—tailored for roles like a network operator or a SOC analyst—we provide immediate clarity on new assets or shifting risk scores, which allows teams to cut through the noise and act on what matters most. To measure this transition from defense to offense, organizations should track the reduction in “mean time to understand” (MTTU) rather than just “mean time to respond.” Success is reflected in the ability to identify a root cause in one click and the percentage of daily tasks that are proactively prioritized by the agentic system before a breach even occurs. This shift ensures that the team is no longer chasing shadows but is instead methodically hardening the environment against known vulnerabilities.
Integrating AI into critical infrastructure requires balancing automation with human-in-the-loop oversight. How do deterministic engines provide a foundation for risk assessment within an agentic framework, and what practical steps can teams take to execute one-click investigations while maintaining strict compliance with evolving global AI regulations?
In critical infrastructure, you cannot afford the unpredictability that sometimes comes with generative models, which is why deterministic engines remain the bedrock of our detection and risk assessment. These engines provide a reliable, logic-based foundation that ensures the agentic framework operates within the bounds of NIST guidance and the EU AI Act. For practical execution, we provide “human-in-the-loop” control, where the AI surfaces the investigation—such as a list of devices impacted by a newly published KEV (Known Exploited Vulnerability)—but the final execution of a response requires a human signature. This one-click capability allows for rapid deep dives into root causes while maintaining a full audit trail for compliance officers. It creates a synergy where the machine handles the heavy lifting of data correlation across 180+ security and IT products, while the human provides the ultimate governance.
Cybersecurity requirements vary significantly between roles, such as biomedical engineers and SOC analysts. In environments spanning IT, OT, and IoMT, how can a unified platform translate technical telemetry into actionable narratives for diverse stakeholders, and what role does proprietary threat intelligence play in this classification process?
The challenge in modern environments like hospitals or factories is that a “risk” looks very different to a biomedical engineer than it does to a network security analyst. Our platform uses GenAI reporting to translate raw telemetry into clear, role-specific narratives that highlight the specific impact on that stakeholder’s domain, whether it’s an infusion pump or a cloud server. Proprietary threat intelligence from Vedere Labs is the “secret sauce” here, as it allows us to classify assets with extreme precision across IT, OT, and IoMT landscapes. This intelligence categorizes behaviors and risks based on real-world data, ensuring that the classification isn’t just a generic label but a contextualized risk score. By mapping these insights directly into the workflow, we bridge the gap between technical data and executive-level decision-making.
Adversaries are increasingly using agentic AI to accelerate attacks, especially as the post-quantum reality approaches. How should organizations restructure their internal defense strategies to counter these faster-moving threats, and what is the specific value of mapping behaviors to frameworks like MITRE ATT&CK during an automated response?
Organizations must move away from the “peak of inflated expectations” regarding standalone AI features and instead adopt integrated systems that mirror how security work actually happens. To counter adversaries who are using AI to speed up their attack cycles, internal defenses must be just as fast, which requires automating the summarization of indicators of compromise and response orchestration. Mapping behaviors to the MITRE ATT&CK® Framework during an automated response is crucial because it provides the “why” behind the “what,” giving analysts a standardized map of the adversary’s playbook. This framework allows the system to not only block an action but to predict the next logical move of the attacker, effectively shortening the response time. By structuring defense around these behavioral patterns, organizations can maintain a resilient posture even as we move toward the complexities of the post-quantum era.
What is your forecast for the future of proactive cyber defense?
I believe we are entering an era where the distinction between “security tools” and “security experts” will blur as agentic AI becomes a permanent, reliable extension of the human team. Within the next few years, the manual triage of fragmented alerts will become a relic of the past, replaced by systems that continuously self-heal and prioritize risk in real-time without needing a single prompt. We will see a shift where cybersecurity is no longer about managing a collection of smarter features, but about managing an integrated system of skills that can think, analyze, and act with the same nuance as a twenty-year veteran. Ultimately, the goal is to reach a state where the defender has a permanent speed advantage over the attacker, turning the tide of the digital arms race through context and automation.
