Security Engineering in the Age of Agentic AI, What Security Teams Need to Know - Practical Playbook

Cybersecurity has been a game of manual patching, reactive incident response, and compliance-driven checklists for years. I’ve spent 15 years in this field and seen how organizations labour over endless log reviews and incident reports. Today, we’re witnessing a new wave - AI & Agentic AI are entering the arena, promising to automate threat detection and even drive decision-making. But before we get swept up in the hype of AI either as a silver bullet or an existential threat, it’s important to recognize that security is still a journey. Just as an athlete tracks their performance metrics to fine-tune their training, security professionals must measure, adapt, and improve their defenses.?

Beyond the Hype: Small AI Steps for Everyday Cybersecurity

The conversation around AI in cybersecurity is often polarized. On one side, there’s the fear that autonomous systems could be manipulated by attackers; on the other, there’s the promise of AI as the ultimate security. The truth lies in practical integration. Instead of waiting for a fully autonomous security system, take small, deliberate steps. For instance, consider automating repetitive tasks like vulnerability scanning and log analysis. Leveraging AI can analyze CloudTrail logs in minutes, flagging anomalies that might take human hours to detect triaging alerts, and correlating them to reduce false positives. This incremental approach doesn’t require a complete overhaul, just the right tweaks that align with your existing workflows.

By starting small, you can experiment with AI tools that complement your security processes. The goal is to enhance your daily operations without relying on technology that isn’t battle-tested. It’s not about letting AI run your security entirely, but about using it as a tool to make your job more efficient and effective.

This blog is not about predicting a future where AI completely replaces human oversight. Instead, it’s about empowering security engineers to take actionable AI steps that streamline their everyday tasks. Whether it’s automating code scanning or enhancing threat detection, the focus here is on incremental improvements that can be implemented right now. Think of it as installing a fitness tracker - small, measurable changes that show you where your vulnerabilities lie and help you improve over time. This approach transforms abstract AI concepts into concrete benefits that save time, reduce risk, and allow you to focus on strategic security initiatives.

AI vs. Agentic AI: Understanding the Cybersecurity Distinction

When we talk about AI in cybersecurity, we usually refer to systems that assist in decision-making by processing vast amounts of data - consider automated log aggregation, vulnerability scanning, or alert filtering. These tools are designed to streamline routine tasks, reduce manual effort, and provide consistent outputs based on preset algorithms. Essentially, they help you do more with less, freeing your team to focus on strategic issues.

Agentic AI, however, takes this a step further. Unlike traditional AI, agentic AI isn’t limited to following static rules. It learns from new data, adapts its detection algorithms as threats evolve, and can even make autonomous decisions - such as predicting attack vectors or suggesting real-time countermeasures. This dynamic capability means that, in theory, an agentic AI system can proactively defend your environment instead of simply alerting you to problems. But with that added power comes a responsibility to ensure that these systems remain transparent, controlled, and resistant to manipulation. The goal is not to replace human oversight but to complement it with adaptive, intelligent defenses.

Getting Started with AI in Cybersecurity: The Essentials

For security engineers new to AI, the first step is to grasp the basics of how AI can augment your daily security operations. Here are a few core concepts and actionable steps to get started:

1. Prompt Engineering and Data Input:

• What It Means: In AI, particularly in large language models, the quality and clarity of your prompts or data inputs can significantly impact the output.
• Actionable Step: Start by ensuring that the data you feed into your AI tools (e.g., logs, code repositories) is clean, well-structured, and relevant. Experiment with different prompts or queries in your threat detection tools to see how they respond to various scenarios.

Prompt Examples:

Threat Detection

Examples -

Bad Prompt:

"Analyze this CloudTrail log and tell me if anything looks suspicious."

It is too vague. The AI has no context on what "suspicious" means in your environment.

Good Prompt: A better prompt provides clear parameters like:

• "Identify failed login attempts from an unusual geographic location in the past 24 hours."
• "Flag all AWS S3 bucket access requests where the ‘Requester’ field is not from our approved list of internal users." "
• Show all CloudTrail log events where root account activities occurred." "
• Detect any changes made to IAM roles with elevated permissions in the last day."
• A more structured and multi-step analysis can be done like: "Analyze the following AWS CloudTrail logs for unusual authentication patterns. Specifically, flag:

1. Logins from new IP addresses outside of usual geographic locations.

2. Multiple failed login attempts followed by a successful login.

3. API calls granting excessive IAM privileges.

Provide a risk rating from 1-10 with a justification for each anomaly."

Automated Incident Response

Examples -

Bad Prompt:

“Create an incident response playbook."

This lacks scope, triggering a generic response rather than a tailored plan.

Good Prompt:

• "Develop an automated incident response playbook that activates if two or more failed SSH login attempts from geographically disparate locations occur within a one-hour period. Include steps to isolate the account temporarily and send detailed notifications to the security dashboard, facilitating both automated and manual follow-ups."

2. Integrating AI into Existing Workflows:

• What It Means: Instead of building new processes from scratch, embed AI into your current CI/CD pipelines or log management systems.
• Actionable Step: Implement automated scanning for vulnerabilities and secrets within your development pipeline. This allows you to incorporate AI gradually, ensuring minimal disruption while improving efficiency.

Example:

Bad Prompt:

• "Run code analysis and show vulnerabilities."
• "List all issues found by CodeQL."

Good Prompt:

• "After executing CodeQL queries on the codebase, identify vulnerabilities where the severity score is unusually high compared to a 30-day historical benchmark. Flag any result where the score increases by more than 20% compared to past averages, and prioritize these for manual review."
• “Analyze CodeQL’s SARIF output to identify and rank vulnerabilities by predicted exploitation likelihood. Highlight vulnerabilities in modules with recent changes or low test coverage and recommend immediate remediation for the top 10% of high-risk findings”.

3. Measuring Effectiveness:

• What It Means: The real value of AI is realized when its outputs are measurable and actionable.
• Actionable Step: Define metrics such as reduction in remediation time or improved detection rates. Use these metrics to refine your approach, ensuring the AI tools are delivering the expected value.

Example:

Bad Example: "Measure how effective the AI is."

Good Example:

1. Mean Time to Detect (MTTD): - Example Target: Reduce MTTD from 12 hours to 30 minutes using AI anomaly detectors.

2. Mean Time to Respond (MTTR): - Example Target: Improve response time to high-severity incidents from 8 hours to 1 hour using automated incident playbooks.

3. False Positive Reduction: - Example Metric: Track the percentage of security alerts flagged by AI that require manual validation. Aim to reduce false positives by 25% within the first quarter.

Agentic AI for Security Engineers: Unlocking Adaptive Defenses

Agentic AI represents the next evolution in cybersecurity, where the system not only automates tasks but also learns and adapts from each incident. For security engineers new to this concept, here’s what you need to know and do:

1. Understand the Learning Component:

• What It Means: Agentic AI uses machine learning models that continuously update based on new data. This means it can refine its threat detection capabilities over time.
• Actionable Step: Begin by deploying agentic AI tools in a monitoring role. For example, set up an agentic anomaly detection system that learns your typical network behavior and flags deviations. Observe its performance and adjust parameters as needed.

2. Establish Autonomous Response Protocols:

• What It Means: These systems can potentially take real-time action, such as isolating a compromised server or adjusting firewall rules based on detected anomalies.
• Actionable Step: Develop automated playbooks that the agentic AI can trigger. Use tools like AWS Lambda for automated responses, but ensure you have manual override capabilities to maintain control.

3. Ensure Transparency and Control:

• What It Means: With increased autonomy, it’s critical to maintain oversight. Agentic AI should offer clear insights into its decision-making process so you can trust its actions.
• Actionable Step: Integrate dashboards that provide visibility into AI-driven decisions, and set up periodic reviews of the model’s performance. This ensures that while the system adapts on its own, it remains aligned with your security policies and risk tolerance.

Deep Dive: Implementing AI and Agentic AI in Cybersecurity Operations

When it comes to modern cybersecurity, integrating AI into your daily operations is no longer a futuristic luxury, it’s a practical necessity. In this section, we’ll explore actionable steps and real-world examples of how to incorporate AI into your security workflows, and then elevate that approach with agentic AI for a more adaptive, autonomous defense system. The goal is to enable security engineers to reduce manual workloads, detect threats faster, and respond to incidents before they escalate.

1. Automated Vulnerability Scanning

Basic AI Approach: Incorporate AI-driven scanning into your CI/CD pipeline to evaluate dependencies, flag critical vulnerabilities, and even suggest fixes automatically. Every time new code is committed, an AI-driven scanner evaluates dependencies, flags critical vulnerabilities, and even generates pull requests to address issues. This process drastically reduces the window of exposure by ensuring vulnerabilities are patched before they can be exploited. For example, by automating vulnerability scanning, you can cut down the average time to remediate critical flaws from 14 days to 7 days.

Agentic AI Enhancement: Agentic AI takes this a step further by continuously learning from new threats and automatically fine-tuning scanning parameters. It can detect subtle code changes or newly discovered vulnerabilities and adjust its scanning algorithms in real-time, ensuring even more accurate and efficient vulnerability management without human intervention.

2. Log Analysis and Anomaly Detection

Basic AI Approach: Instead of manually sifting through endless logs, set up an AI-based system to ingest AWS CloudTrail data and use machine learning algorithms to detect anomalies. For instance, if a user logs in from an unexpected location or at an odd hour, the system triggers an alert—potentially reducing Mean Time to Detect (MTTD) from days to under an hour. This approach enables your security team to act on suspicious activity swiftly.

Agentic AI Enhancement: Agentic AI doesn't just analyze static logs, it continuously adapts its detection models based on evolving threat patterns. By autonomously learning from each incident, agentic AI can adjust thresholds and detection rules in real-time, minimizing false positives and ensuring that genuine anomalies are flagged immediately. This dynamic behavior ensures that your system remains effective even as attacker tactics evolve.

3. Automated Incident Response

Basic AI Approach: Develop automated playbooks using tools like Tines or AWS Lambda functions. When the system detects a breach or suspicious activity, these playbooks can automatically isolate the affected resources and notify your team through Slack or another alert system. This proactive automation helps ensure that even if human response is delayed, the system begins mitigating damage immediately.

Agentic AI Enhancement: With agentic AI, incident response can be taken to a whole new level. Imagine an autonomous system that not only isolates compromised servers but also analyzes the breach context, adjusts security policies on the fly, and even recommends improvements to your incident response plan. By learning from every incident, agentic AI refines its own response strategies—making your defense posture increasingly robust over time.

I’m actively developing custom tools that harness agentic AI to further automate these. The solutions are designed to plug into your existing workflows and evolve with your startup's needs. If you’re interested in collaborating or learning more about integrating agentic AI into your security operations, I’d love to connect.

Use Case for CISOs: Strategic Oversight in the Age of AI

For CISOs, the integration of AI into cybersecurity is not just about automating tasks, it’s about enhancing strategic oversight. Imagine a dashboard that not only reports on past incidents but also predicts future threats based on current trends. This predictive capability can help CISOs allocate resources more effectively, improve incident response strategies, and build stronger, data-driven security policies. While I’ll dive deeper into this in a future post, know that the principles of AI-driven security are already reshaping how we approach risk management at the enterprise level.

List of Tools To Get You Started: Below is a curated list of AI-driven tools categorized by function, helping security engineers streamline automation, incident response, and security operations. Each of these tools serves a specific purpose, whether it's writing better code, building AI agents, automating security workflows, or running LLM models efficiently.

1. AI-Enhanced Development Environments (IDE)

These tools improve coding efficiency by offering AI-assisted suggestions, debugging help, and automation.

• CursorAI (Paid) – An AI-powered IDE that enhances productivity by integrating AI-assisted coding directly into your development environment.
• VS Code + GitHub Copilot (Free/Open-Source) – A widely used combination that provides AI-powered coding suggestions within VS Code.

2. AI Agent Frameworks

If you want to build, manage, and deploy AI-driven security automation, these frameworks make it seamless.

• Agno AI – A user-friendly AI agent framework that simplifies creating and managing AI-driven security automation.
• CrewAI – An open-source framework for developing AI agents with multi-step workflows.

3. No-Code AI Agents & Workflow Automation

For security teams that want to automate processes without deep coding knowledge, these no-code tools allow for visual AI agent creation.

• n8n.io – A no-code/low-code automation platform that integrates AI agents with security tools.
• Make.com – A powerful visual workflow builder that allows integration of GitHub, Slack, AWS SDK, Gmail, and more.

4. Browser Automation for Security Testing

Automating repetitive security tasks, such as testing web applications, is key.

• Playwright – A robust tool for automating browsers, useful for security testing and monitoring.

5. LLM APIs for Custom AI Integration

If you're working with large language models (LLMs) for security, these APIs provide powerful integrations.

• Groq – High-performance inference engine for AI workloads.
• OpenRouter – A routing API for accessing various open-source and proprietary LLMs.
• Ollama – A locally run LLM framework, ideal for those with high-performance hardware looking to keep AI operations on-prem.

6. Minimalist UI for AI-Driven Security Dashboards

Want a simple, interactive UI for security automation? These tools help create clean, functional interfaces.

• Streamlit – Build fast, interactive, and data-driven dashboards

To Conclude:

Proactive security teams don’t wait for a breach to assess their defenses, in fact, they continuously track and improve their security posture to stay ahead of threats. By integrating AI (especially agentic AI) into your cybersecurity processes, you build an adaptive, measurable system that not only defends your startup but also drives continuous improvement. Key Takeaways from this Blog:

• Proactive AI Integration: Use AI to automate routine tasks like vulnerability scanning and log analysis, enabling early detection of threats.
• Empower Security Engineers: Small, actionable AI steps can streamline workflows and reduce manual overhead, letting your team focus on strategic initiatives. The list of tools can be leveraged for daily workflow enhancements.
• Measure and Adapt: Leverage both leading and lagging metrics to continuously improve your security posture, ensuring your defenses evolve as threats do.

This isn’t about abandoning human oversight; it’s about using AI to complement and enhance it. From automating vulnerability scans to predicting threats and streamlining incident response, the key is to start small and iterate. In the fast-evolving world of cybersecurity, every proactive step counts.

So, how will you begin using AI & Agentic AI to make your security smarter, faster, and more resilient?