FAQs about Agentic AI

What is agentic AI, and how does it differ from traditional AI in cybersecurity? Agentic AI is a term used to describe autonomous, goal-oriented, systems that are able to perceive their environment, take decisions, and act to achieve specific goals. Unlike traditional AI, which is often rule-based or reactive, agentic AI systems can learn, adapt, and operate with a degree of independence. Agentic AI is a powerful tool for cybersecurity. It allows continuous monitoring, real time threat detection and proactive response.
How can agentic AI improve application security (AppSec?)  https://notes.io/wJuSY ? Agentic AI has the potential to revolutionize AppSec by integrating intelligent agents within the Software Development Lifecycle (SDLC). These agents can continuously monitor code repositories, analyze commits for vulnerabilities, and leverage advanced techniques like static code analysis, dynamic testing, and machine learning to identify a wide range of security issues. Agentic AI prioritizes vulnerabilities according to their impact in the real world and exploitability. This provides contextually aware insights into remediation. What is a code property graph (CPG), and why is it important for agentic AI in AppSec? A code property graph (CPG) is a rich representation of a codebase that captures relationships between various code elements, such as functions, variables, and data flows. By building a comprehensive CPG, agentic AI can develop a deep understanding of an application's structure, potential attack paths, and security posture. This contextual awareness allows the AI to make better security decisions and prioritize vulnerabilities. It can also generate targeted fixes.  AI-powered automatic vulnerability fixing leverages the deep understanding of a codebase provided by the CPG to not only identify vulnerabilities but also generate context-aware, non-breaking fixes automatically. The AI analyzes the code surrounding the vulnerability, understands the intended functionality, and crafts a fix that addresses the security flaw without introducing new bugs or breaking existing features. This method reduces the amount of time it takes to discover a vulnerability and fix it. It also relieves development teams and provides a reliable and consistent approach to fixing vulnerabilities.  What potential risks and challenges are associated with the use of agentic AI for cybersecurity? Some potential challenges and risks include:

Ensure trust and accountability for autonomous AI decisions
Protecting AI systems against adversarial attacks and data manipulation
Maintaining accurate code property graphs
Ethics and social implications of autonomous systems
Integrating AI agentic into existing security tools
How can organizations ensure that autonomous AI agents are trustworthy and accountable in cybersecurity? Organizations can ensure the trustworthiness and accountability of agentic AI by establishing clear guidelines and oversight mechanisms. This includes implementing robust testing and validation processes to verify the correctness and safety of AI-generated fixes, maintaining human oversight and intervention capabilities, and fostering a culture of transparency and responsible AI development. Regular audits and continuous monitoring can help to build trust in autonomous agents' decision-making processes.  Best practices for secure agentic AI development include:

Adopting safe coding practices throughout the AI life cycle and following security guidelines
Implementing adversarial training and model hardening techniques to protect against attacks
Ensuring data privacy and security during AI training and deployment
Validating AI models and their outputs through thorough testing
Maintaining transparency and accountability in AI decision-making processes
AI systems should be regularly updated and monitored to ensure they are able to adapt to new threats and vulnerabilities.
By continuously monitoring data, networks, and applications for new threats, agentic AI can assist organizations in keeping up with the rapidly changing threat landscape. These autonomous agents can analyze vast amounts of security data in real-time, identifying new attack patterns, vulnerabilities, and anomalies that might evade traditional security controls. Agentic AI systems provide proactive defenses against evolving cyber-threats by adapting their detection models and learning from every interaction. What role does machine learning play in agentic AI for cybersecurity? Agentic AI is not complete without machine learning. It allows autonomous agents to identify patterns and correlate data and make intelligent decisions using that information. Machine learning algorithms are used to power many aspects of agentic AI including threat detection and prioritization. They also automate the fixing of vulnerabilities. Machine learning improves agentic AI's accuracy, efficiency and effectiveness by continuously learning and adjusting. How can agentic AI improve the efficiency and effectiveness of vulnerability management processes? Agentic AI automates many of the laborious and time-consuming tasks that are involved in vulnerability management. Autonomous agents are able to continuously scan codebases and identify vulnerabilities. They can then prioritize these vulnerabilities based on the real-world impact of each vulnerability and their exploitability. The agents can generate context-aware solutions automatically, which reduces the amount of time and effort needed for manual remediation. Agentic AI allows security teams to respond to threats more effectively and quickly by providing actionable insights in real time.

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

Platforms that automatically detect and respond to malicious threats and continuously monitor endpoints and networks.
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
Autonomous incident response tools that can contain and mitigate cyber attacks without human intervention
AI-driven solutions for fraud detection that detect and prevent fraudulent activity in real time
How can agentic AI bridge the cybersecurity skills gap and ease the burden on security team? 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. Agentic AI's insights and recommendations can also help less experienced security personnel to make better decisions and respond more efficiently to potential threats.  Agentic AI can help organizations meet compliance and regulatory requirements more effectively by providing continuous monitoring, real-time threat detection, and automated remediation capabilities. Autonomous agents can ensure that security controls are consistently enforced, vulnerabilities are promptly addressed, and security incidents are properly documented and reported. 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.  To successfully integrate agentic AI into existing security tools and processes, organizations should:

Assess their current security infrastructure and identify areas where agentic AI can provide the most value
Create a roadmap and strategy for the adoption of agentic AI, in line with security objectives and goals.
Ensure that agentic AI systems are compatible with existing security tools and can seamlessly exchange data and insights
Provide training and support for security personnel to effectively use and collaborate with agentic AI systems
Create governance frameworks to oversee the ethical and responsible use of AI agents in cybersecurity
What are some emerging trends in agentic AI and their future directions? Some emerging trends and directions for agentic artificial intelligence in cybersecurity include:

Collaboration and coordination among autonomous agents from different security domains, platforms and platforms
AI models with context-awareness and advanced capabilities that adapt to dynamic and complex 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?  https://carey-robb.hubstack.net/frequently-asked-questions-about-agentic-artificial-intelligence-1742960591  provides a powerful defense for APTs and targeting attacks by constantly monitoring networks and systems to detect subtle signs of malicious behavior. Autonomous agents are able to analyze massive amounts of data in real time, identifying patterns that could indicate a persistent and stealthy 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.

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

Monitoring of endpoints, networks, and applications for security threats 24/7
Prioritization and rapid identification of threats according to their impact and severity
Security teams can reduce false alarms and fatigue by reducing the number of false positives.
Improved visibility into complex and distributed IT environments
Ability to detect new and evolving threats which could evade conventional security controls
Security incidents can be dealt with faster and less damage is caused.
How can agentic AI improve incident response and remediation processes? Agentic AI has the potential to enhance incident response processes and remediation by:

Automatically detecting and triaging security incidents based on 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, more consistent incident remediation and reducing the impact of security breaches
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
Foster a culture of collaboration and continuous learning, encouraging security personnel to work alongside AI systems and provide feedback for improvement
Develop clear protocols and guidelines for human-AI interaction, including when to trust AI recommendations and when to escalate issues for human review
Invest in programs to help security professionals acquire the technical and analytic skills they need to interpret and act on AI-generated insights
Encourage cross-functional collaboration between security, data science, and IT teams to ensure a holistic approach to agentic AI adoption and use


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 methods for high-risk security scenarios such as incident response or 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