How to Improve Security With Automation

Most companies still follow the old cybersecurity playbook: 1. Buy antivirus 2. Trust the default firewall 3. Hope a data breach never happens 4. React chaotically when it does 5. Spend even more after damage is done The new, AI-driven cybersecurity approach flips this: 1. Proactively identify threats 2. Use AI for threat intelligence and gap analysis 3. Implement zero-trust architecture 4. Automate detection and response 5. Continuously refine with real-time data The hard truth? Most data breaches (and the resulting financial devastation) happen because organizations rely on outdated, reactive measures. But that was before AI. I’ve spent years mitigating breaches that could have been prevented with proactive measures. Now, with the right AI-driven framework, you can avert catastrophic threats in days, not months. Here’s my 5-step AI-enabled cybersecurity framework to save your company from hefty fines, lost trust, and public embarrassment: 1. Asset Discovery & Prioritization • Use AI-powered scanners (like Censys or Shodan) to find every exposed asset you have. • Feed the list into ChatGPT or other AI tools to categorize them by risk level. • If you don’t know what you’re defending, you’ve already lost. 2. Threat Intelligence & Gap Analysis • Tap into threat intel feeds (MITRE ATT&CK, VirusTotal, open-source repos). • Ask AI to compare your network or app vulnerabilities against known exploits. • No deep intel on emerging threats? That’s a glaring gap. 3. Automated Penetration Testing • Old approach: hire pen testers once or twice a year. • New approach: continuous AI-driven pentests that probe your environment 24/7. • If the AI tool cracks through your defenses easily, it’s time to upgrade your armor. 4. Zero-Trust Implementation • Grant “least privileged” access—no one gets more than they absolutely need. • Use AI to monitor user behaviors for anomalies (e.g., logging in from new locations, odd times). • Trust but verify. Actually, don’t trust—verify everything. 5. Incident Response Optimization • Replace static incident playbooks with AI-updated procedures. • Use machine learning to accelerate root cause analysis. • Automate common remediation steps. • If your IR plan is collecting dust in a binder, you’re already behind the curve. This isn’t just a few security patches—it’s a transformative shift. AI makes cybersecurity continuous, adaptive, and deeply data-driven. The result? • Fewer vulnerabilities slipping through the cracks • Faster response times for any incidents that do occur • Significantly reduced risk of financial and reputational damage You can keep plugging holes after breaches happen—or harness AI to build a virtually watertight security posture before it’s too late. … It’s your move. …

Still trying to manage your ever-increasing alert flow by hiring more analysts? That’s much like adding buckets to deal with a leaking roof. Invest in detection engineering and automation engineering to reduce the alert flow and prevent alert fatigue and unhappy analysts. Here are some best practices: - Apply an automation-first strategy: handle and/or accelerate all alerts through automation - Continuously tune and optimize detection rules - Let analysts and detection / automation engineers work closely together to increase the effectiveness of engineering efforts - Establish metrics for rule quality to identify candidates for tuning and automation - Test against defined quality criteria before putting any detection rules live - Increase the fidelity of your rules by alerting on more specific criteria - Aggregate and analyse batches of noisy alerts daily or weekly, instead of handling them individually in real-time - Consider your ideal ratio between analysts and engineers. Start out with 50-50, then decide what would best suit your needs - Make risk-based decisions on added value of rules compared to time investment, and drop time-consuming rules with little added value if they cannot be tuned properly This is by no means an easy thing to do. But by focussing on engineering and detection quality, you can transition to a state where you control of the alert flow instead of the other way around, so that analysts can focus on the alerts that truly matter. #soc #securityoperations #securityanalysis #detectionengineering #automationfirst

Is your SOC understaffed — or under-automated? Many security leaders assume the answer is headcount. More analysts, more coverage, better outcomes. But the real constraint has never been people. It's been the model — one built around human triage of infinite alerts, where severity thresholds exist not because of risk logic, but because the team couldn't physically handle the volume. AI SOC changes that equation. But only if you run it the right way. Here are 5 best practices shared by Jon Hencinski and Gourav Nagar from deploying AI-enabled security operations: 1️⃣ Investigate everything, not just what's "high severity" When AI handles the investigative workload, severity becomes an input — not a triage gate. Low-severity signals get worked while they're still early indicators. The backlog disappears as a permanent operating condition. 2️⃣ Enforce investigative consistency Human analysts vary by fatigue, experience, and time of day. AI automates and documents every step in the investigation — every single time. That consistency turns output anomalies into real signals, not artifacts of human variance. 3️⃣ Expand your detection library aggressively Engineers hesitate to write more detections because the SOC can't handle the volume. With AI, that constraint disappears. Deploy behavioral rules with high false positive rates if they occasionally catch critical breaches. AI handles the noise. You get the coverage. 4️⃣ Don't rush to full autonomy Automated investigation ≠ automated remediation. Banning IPs or disabling accounts without a human decision gate can cause outages harder to unwind than the original threat. Optimize for decision support first — let AI gather evidence at machine speed, then hand high-impact actions to humans. 5️⃣ Validate with a parallel run Trust in an autonomous system must be statistical, not anecdotal. Run a 15-30 day test where AI processes the same queue as your team. Compare verdict accuracy, data sources examined, and conclusions reached. Move from "I think it works" to "the data proves it works." The goal isn't to replace analysts. It's to move manual and repetitive tasks off the human queue — so your team spends time where it actually changes outcomes. Think role elevation, not role elimination.

One of the most pressing challenges in SOCs today is the overwhelming volume of alerts, leading to alert fatigue and analysts missing critical threats. High false positive rates and outdated detection rules further exacerbate this issue, reducing efficiency. To address these challenges, a strategic focus on detection engineering and automation is crucial. To enhance your threat detection capabilities, start by mapping your existing use cases to the MITRE ATT&CK framework if it's not already in place. This provides a structured approach to identifying gaps in coverage. After this, review the false positive ratio of your current detection rules to assess their effectiveness and prioritize optimization. While striving for comprehensive MITRE ATT&CK coverage is a worthy goal, it's important to recognize that achieving 100% coverage is not feasible. Instead, leverage threat intelligence to focus on the TTPs (Tactics, Techniques, and Procedures) most commonly used by adversaries targeting your environment. Start by addressing these high-priority TTPs. However, coverage should not be seen as binary. Simply having a detection rule for a TTP does not mean full coverage—depth of coverage is key. Before expanding coverage to additional TTPs, ensure your current data sources are adequate to cover the targeted techniques. This foundational step ensures that your coverage is based on reliable and relevant data. Once this is in place, you can expand your coverage to include additional TTPs, even those beyond your immediate threat landscape, by incorporating more data sources and threat intelligence. Key Practices for Optimizing Threat Detection: 1- Prioritize an automation-first strategy, handling alerts through automated workflows wherever possible. 2- Continuously refine and optimize detection rules to minimize false positives and improve effectiveness. 3- Foster collaboration between analysts and detection/automation engineers to enhance the detection engineering process. 4- Establish metrics to evaluate the quality of detection rules, focusing on those that provide the most value and can be improved over time. 5- Increase the specificity of detection rules to reduce noise and improve the signal-to-noise ratio. 6- Aggregate noisy alerts for batch analysis rather than addressing them individually in real-time, improving resource efficiency. 7- Make risk-based decisions when prioritizing rule development, removing or refining low-value rules that consume excessive resources. Focusing on detection engineering and automation will allow you to manage alert flow more effectively, ensuring that analysts can dedicate their attention to the alerts that truly matter. This shift will enhance threat detection maturity, improve operational efficiency, and reduce the impact of alert fatigue. #Cybersecurity #MITRE #ThreatDetection #Automation #SOC #DetectionEngineering #ThreatHunting #CyberDefense #SIEM #SecurityOperations #IncidentResponse

𝐇𝐨𝐰 𝐀𝐈 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐝 𝐌𝐲 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐓𝐞𝐚𝐦'𝐬 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 The numbers tell the story: my team processes 600,000 security incidents yearly through automation. This work would require 200+ analysts using traditional methods. We do it with 6. This isn't about replacing security professionals—it's enabling them to scale impossibly. Our analysts evolved from alert responders to strategic defenders. They focus on threat hunting, engineering, and architecture instead of repetitive triage. We've implemented behavioral-based detection through CrowdStrike, SOAR platforms running 200+ playbooks, and AI-driven tools like DarkTrace and Abnormal. CrowdStrike just announced Charlotte Agentic SOAR—intelligent agents that "reason, decide, and act in real time." Omdia's research suggests autonomous SOC evolution may become standard within 1-2 years. But automation doesn't replace expertise—it's a force multiplier. I've restructured my team so junior staff spend 25% on operations and 75% on engineering and threat hunting. My long-term strategy: position security as an enabler of AI, not a blocker. As AI becomes ubiquitous, securing AI connections becomes a core responsibility. How are you leveraging AI in security operations? #ArtificialIntelligence #FutureOfWork

#AutoCon3 How We Eliminated Security Vulnerabilities with Network Automation—And That’s Just the Beginning! I love a good security story. Lee Harper of Terracon presented on how they're using network automation to help deal with CVEs and vulnerabilities. Terracon is an engineering consultancy. They had over 120 locations across the continental US around 2019. The network team had 4 members, and they were busy, with no opportunity to add headcount. So they turned to Gluware to help with automation. Their approach to automation:  1. Focus on acute pain 2. Select use cases that deliver outcomes the fastest 3. Quantify results to secure ongoing sponsorship 4. Brace for momentum They started with inventory management. Took about a month to onboard inventory management. Then they worked on standardizing configs. They started with static configs for things like DNS and NTP that didn’t change all that much. However, they ran into OS management issues pretty quickly. They had different OS versions on the same hardware platform. They settled on a single OS version, took 3 to 4 months to push out, including lots of testing to see impact of new OS. They didn’t have people on site, so you can’t have upgrade failures at remote locations. Once that was standardized, they went back to configuration automation. Phase 2: Maintaining the Network Config Drift Audit: They validate configuration changes to make sure people aren’t messing up during manual changes, and validating OS update changes and syntax changes. Upper management has more confidence in OS updates because the network team has made them safer. Security Use Case: When a vulnerability gets announced, the first thing is discovery. You need that inventory and config state to understand whether a vulnerability actually affects you. Remediation: They have config templates, so that if a vulnerability comes out and requires an update, they can make the change in your template and push it out. This gives them a faster response Reporting: If you are in a regulated industry, documentation is critical. They can report on vulnerability remediation to show they're compliant Phase 2 Results: Terracon now has 200 locations and the same number of team members. They are also able to save time and respond faster. They can update firmware across the network in one night. And they can validate after the change to make sure everything is online and working Phase 3: Anticipated Results 1. More consistent configuration across platforms with less human error 2. Quicker incident response 3. Better communication between teams 4. Bring automation to fringe systems Final Thoughts: They didn’t go into this thinking of automation as a security tool. They wanted to free up resources. As they got automation working, their security posture improved because of better response time to OS bugs, config changes, and firmware updates. Automation made security easier.

Recently, I did an exercise on what to automate first in cybersecurity workflows, and it made me realise something important—I might have been approaching it the wrong way for years. Typically, I would jump straight into automating incident response (IR) playbooks, categorising and splitting them into familiar buckets: (link in the comments because LinkedIn’s algorithm seems to hide posts with links—don’t get me started on that!) ▶ Context Enhancement Automations ▶ Supportive Task Automations ▶ Orchestration But as I started mapping out the steps and processes, I noticed something surprising. In about 90% of cases, the real need was in automating supportive tasks, especially those related to compliance. This made me reconsider my approach—should the first step in automation actually be focused on compliance? After all, a significant percentage of cyberattacks and data breaches stem from simple misconfigurations. When I checked out the Blinkops library, I found over 1,000 compliance automation ideas—more than any other category. Compliance automation isn't just a checkbox; it could very well be the foundation that prevents many incidents before they happen. I’d love to hear your thoughts—what’s the first area you usually automate?