Meta’s Llama Framework Flaw Exposes Critical Risks
January 27, 2025
SOC analysis has never been a simple task. With thousands of security alerts, many of which are false positives, analysts (and occasionally IT teams working as SecOps) must attempt to sort through the deluge of daily notifications to find a small number of genuine risks. Due to the constant, nonstop labor, there is a greater chance of missing important security events, alert fatigue, and desensitization. Seventy percent of SOC analysts report high levels of stress, and sixty-five percent say they would quit their employment within a year. Because of this, retention is a significant problem for security teams, particularly given the current scarcity of qualified security analysts.
On the operational side, analysts devote more effort to manual, repetitive duties such as resolving and documenting incidents and investigating alarms than they do to proactive security measures. Because the cyber ecosystem is changing so quickly, security teams find it difficult to configure and manage SOAR playbooks. In addition, analysts are forced to navigate disparate security systems due to tool overload and compartmentalized data, which causes difficulty and more importantly misses correlations between events that could have been used to find true positives.
AI-Powered Threat Actors – Yikes!
The fact that threat actors are using AI to fuel their criminality adds to the aforementioned. They can launch more effective, adaptive, and challenging-to-detect attacks at scale thanks to AI’s ability to process enormous volumes of data quickly. Artificial intelligence (AI) tools produce social engineering scripts, deepfake content, and incredibly convincing phishing emails, making deception considerably simpler, even for novice attackers. They can also employ AI to automate vulnerability discovery by scanning vast codebases for exploitable holes, developing sophisticated malware, and reverse engineer security systems. AI-powered chatbots can also pose as actual users, carry out extensive fraud, and give beginners step-by-step instructions on how to commit cybercrimes.
Attackers have lowered the average breakout time for successful incursions from 79 minutes to 62 minutes, with the shortest known breakout time being just 2 minutes and 7 seconds, according to a 2024 Crowd Strike analysis. The sheer volume and pace of today’s cyber-attacks still force SOC teams to work quicker than ever before and somehow manually analyze and triage the absurd number of alerts being created, even with the best detection tools and dozens of analysts available (a dream scenario). It has truly been a difficult task. However, it is no longer the case.
The Modern SOC Returns: The Ideal Combination of AI and Human-in-the-Loop
If you are a CISO or SOC analyst, you are aware that I did not overstate how serious the problem is. However, things are changing. Human teams will be able to process any kind and number of security warnings with the help of new AI tools for SOCs, freeing them up to rapidly address actual threats. An example of what some early adopters are going through is seen here.
Automated Triage
Automated security alert triage is now available from several providers, greatly reducing the quantity of notifications that require human analysts’ attention. The best situation is for an AI-powered SOC analyst to be able to decipher any kind of security alert from any sensor or defense system, even though several vendors provide automated triage for particular use cases like phishing, endpoint, network, and cloud (with the triage playbook developed by human security professionals). This makes it possible to thoroughly triage all security events, from the most frequent to the least known. The AI triage’s real logic, down to every step taken, is easily accessible for a human analyst to evaluate if they so want, demonstrating the need for transparency in this situation as well.
Complete Command over How to Respond to Actual Threats
It is crucial to have a human in the loop to review the suggested remediation and can accept, modify, or immediately implement it, even though an AI-powered SOC platform generates an accurate response appropriate for the specific threat (providing similar value to a SOAR without all the configuration and maintenance headache).
DeepSeek, also known as ChatGPT, Joins the Team
SOC teams can investigate new risks, the most recent attack techniques, and the most effective ways to counter them by utilizing generative AI. Analysts will find it easier to obtain and quickly learn about pertinent solutions with the use of tools like ChatGPT, which are amazing for quickly ramping up on almost any issue, including security.
Log interpretation, anomaly detection, and data querying
SOC analysts don’t have to worry about query syntax anymore. Alternatively, they can utilize natural language to locate the information they require, and AI technologies can instantly clarify the meaning of a given log or dataset. A built-in anomaly detection system helps to find odd patterns that may need more research while examining a collection of thousands of logs.
For data-hungry AI, more data is needed. Not with a Crazy Bill.
For data-hungry AI, more data is needed. Not with a Crazy Bill.
Because they use enormous volumes of data to identify trends, generate forecasts, and gradually increase their accuracy, AI systems are data-hungry. Traditional data storage, however, might be prohibitively expensive. Rapid querying of logs and other data from extremely cheap cold storage, like AWS S3, is now feasible thanks to emerging technology. In order to automatically triage alarms, these AI-powered SOC platforms must be able to quickly access, process, and interpret large volumes of data. The same is true for people. You may now give your analysts quick querying capabilities and limitless retention for compliance requirements, all while maintaining complete control over your data as a CISO or VP of security, free from vendor lock-in.
Everything will simply proceed more quickly.
Social interactions were slower in the previous century; you had to meet in person, send a letter and wait days for a response, or call someone’s landline and hope they answered. By 2025, social media, AI-powered communication, and instant messaging have made communication smooth and instantaneous. Security activities are undergoing the same change. Response times are slowed down by traditional SOCs’ reliance on manual triage, drawn-out investigations, and intricate SOAR settings. However, analysts no longer need to manually create remediation actions or sort through countless alarms thanks to AI-powered SOC solutions. AI significantly reduces workload and response times by automating triage, validating actual threats, and suggesting exact remedies.
In conclusion, SOC analysts experience stress and inefficiency as a result of their struggles with alert levels, manual triage, and growing cyber threats. Rapid reaction is now more important than ever as threat actors use AI to automate attacks. The good news is that with AI-powered triage, automated remediation, and natural language-driven data querying, the modern SOC is developing and freeing analysts from laborious procedures to concentrate on actual threats. The SOC is getting smarter, faster, and more scalable thanks to AI.
