Agentic Artificial Intelligence FAQs

What is agentic AI, and how does it differ from traditional AI in cybersecurity? Agentic AI refers to autonomous, goal-oriented systems that can perceive their environment, make decisions, and take actions to achieve specific objectives. Unlike traditional AI, which is often rule-based or reactive, agentic AI systems can learn, adapt, and operate with a degree of independence. In cybersecurity, agentic AI enables continuous monitoring, real-time threat detection, and proactive response capabilities.

What are some examples of real-world agentic AI in cybersecurity? Agentic AI is used in cybersecurity.

Autonomous threat detection and response platforms that continuously monitor networks and endpoints for malicious activity
AI-powered vulnerability scanners that identify and prioritize security flaws in applications and infrastructure
Intelligent threat intelligence systems that gather and analyze data from multiple sources to provide proactive defense against emerging threats
Automated incident response tools can mitigate and contain cyber attacks without the need for human intervention
AI-driven solutions for fraud detection that detect and prevent fraudulent activity in real time
Agentic AI helps to address the cybersecurity skills gaps by automating repetitive and time-consuming security tasks currently handled manually. Agentic AI systems free human experts from repetitive and time-consuming tasks like continuous monitoring, vulnerability scanning and incident response.  this link  and recommendations can also help less experienced security personnel to make better decisions and respond more efficiently to potential threats. What are the implications of agentic AI on compliance and regulatory requirements for cybersecurity? Agentic AI helps organizations to meet compliance and regulation requirements more effectively. It does this by providing continuous monitoring and real-time threat detection capabilities, as well as automated remediation. Autonomous agents ensure that security controls and vulnerabilities are addressed promptly, security incidents are documented, and reports are made. The use of agentic AI raises new compliance concerns, including ensuring transparency, accountability and fairness in AI decision-making, as well as protecting privacy and security for data used to train and analyze AI. How can organizations integrate AI with their existing security processes and tools? For organizations to successfully integrate agentic artificial intelligence into existing security tools, they should:

Assess their current security infrastructure and identify areas where agentic AI can provide the most value
Develop a clear strategy and roadmap for agentic AI adoption, aligned with overall security goals and objectives
Ensure that agentic AI systems are compatible with existing security tools and can seamlessly exchange data and insights
Support and training for security personnel in the use of agentic AI systems and their collaboration.
Establish governance frameworks and oversight mechanisms to ensure the responsible and ethical use of agentic AI in cybersecurity
Some emerging trends and future directions for agentic AI in cybersecurity include:

Collaboration and coordination among autonomous agents from different security domains, platforms and platforms
Development of more advanced and contextually aware AI models that can adapt to complex and dynamic security environments
Integrating agentic AI into other emerging technologies such as cloud computing, blockchain, and IoT Security
To protect AI systems, we will explore novel AI security approaches, including homomorphic cryptography and federated-learning.
AI explained techniques are being developed to increase transparency and confidence in autonomous security decisions
How can agentic AI help organizations defend against advanced persistent threats (APTs) and targeted attacks? Agentic AI can provide a powerful defense against APTs and targeted attacks by continuously monitoring networks and systems for subtle signs of malicious activity. Autonomous agents can analyze vast amounts of security data in real-time, identifying patterns and anomalies that might indicate a stealthy and persistent threat. Agentic AI, which adapts to new attack methods and learns from previous attacks, can help organizations detect APTs and respond more quickly, minimising the impact of a breach.

The benefits of using agentic AI for continuous security monitoring and real-time threat detection include:

24/7 monitoring of networks, applications, and endpoints for potential security incidents
Rapid identification and prioritization of threats based on their severity and potential impact
Reduced false positives and alert fatigue for security teams
Improved visibility into complex and distributed IT environments
Ability to detect novel and evolving threats that might evade traditional security controls
Security incidents can be dealt with faster and less damage is caused.
Agentic AI can significantly enhance incident response and remediation processes by:

Automated detection and triaging of security incidents according to their severity and potential impact
Contextual insights and recommendations to effectively contain and mitigate incidents
Orchestrating and automating incident response workflows across multiple security tools and platforms
Generating detailed incident reports and documentation for compliance and forensic purposes
Continuously learning from incident data to improve future detection and response capabilities
Enabling faster and more consistent incident remediation, reducing the overall impact of security breaches
What are some considerations for training and upskilling security teams to work effectively with agentic AI systems? To ensure that security teams can effectively leverage agentic AI systems, organizations should:

Provide comprehensive training on the capabilities, limitations, and proper use of agentic AI tools
Encourage security personnel to collaborate with AI systems, and provide feedback on improvements.
Create clear guidelines and protocols for human-AI interactions, including when AI recommendations should be trusted and when issues should be escalated to human review.
Invest in upskilling programs that help security professionals develop the necessary technical and analytical skills to interpret and act upon AI-generated insights
To ensure an holistic approach to the adoption and use of agentic AI, encourage cross-functional collaboration among security, data science and IT teams.


How can we balance the benefits of AI and human decision-making with the necessity for human oversight in cybersecurity? To strike the right balance between leveraging agentic AI and maintaining human oversight in cybersecurity, organizations should:

Establish clear roles and responsibilities for human and AI decision-makers, ensuring that critical security decisions are subject to human review and approval
Use AI techniques that are transparent and easy to explain so that security personnel can understand and believe the reasoning behind AI recommendations
Test and validate AI-generated insights to ensure their accuracy, reliability and safety
Maintain human-in-the-loop approaches for high-stakes security scenarios, such as incident response and threat hunting
Foster a culture of responsible AI use, emphasizing the importance of human judgment and accountability in cybersecurity decision-making
Regularly monitor and audit AI systems to identify potential biases, errors, or unintended consequences, and make necessary adjustments to ensure optimal performance and alignment with organizational security goals