The battle didn’t begin today, and it’s not ending anytime soon, as cyber threats are changing at a rate never seen before. Today, attackers are getting more and more creative, utilizing tools like AI to exploit vulnerabilities at a rate faster than security teams can identify and address them.
Meanwhile, security teams are stuck drowning in intelligence reports, red team findings, and endless data streams – expected to extract relevant insight, prioritize risks, and then roll out detections before the next hits. But by the time they reach that point, attackers are already ten steps ahead.
Over 60% of organizations struggle with real-time threat detection. Security teams are overwhelmed, drowning in millions of indicators of compromise (IOCs) each day, with false positives exceeding 50% of all alerts. Meanwhile, attackers leverage AI automation to execute large-scale attacks in seconds, adapting faster than security teams can respond.
This only means one thing: the old way of doing things, like manual analysis, long research cycles, static prioritization, and reactive defenses, just doesn’t cut it anymore. If security teams don’t evolve, they won’t just be behind but will be defenseless.
The answer is simple – A radical shift. Adaptive Threat Intelligence (ATI) is that shift, bringing automation and intelligence to threat research so security teams can finally keep up with attackers.
Why Security Teams Are Losing the Race Against Attackers
Before answering this question, let’s examine the importance of threat research and its place in the detection landscape. Typically, the threat detection process entails collecting data from several system sources, creating a baseline of consistent activity, examining data for irregularities or questionable trends, and then looking into possible risks identified by the system. Threat research is essentially the proactive intelligence collection that guides the detection process itself; it aids in identifying new attack techniques and updating detection algorithms to keep ahead of changing threats. Despite being the cornerstone of proactive security, it currently acts as a bottleneck that slows down everything. Back to our question, what’s broken:
- The Research Process is Too Slow
Threat research is painfully manual. Analysts spend hours combing through reports, trying to extract meaningful insights.
- The average security team spends 8 hours analyzing a single threat report.
- A single analyst handles dozens of weekly reports, leading to hundreds of hours lost on manual work.
- Even then, research is often outdated by the time it’s done because attackers have already evolved.
Meanwhile, attackers automate their tactics, running exploits at scale, shifting attack vectors, and evading traditional defenses in minutes.
- Prioritization is a Mess
Security teams must identify which risks are most important, not just which ones are out there. Prioritization is a mess right now:
- 78% of organizations lack confidence in their ability to identify high-risk threats.
- 60% have poor visibility into credential stuffing attacks, fake account creation, and API abuse.
- 50% don’t know when attackers use stolen API keys to mimic legitimate requests.
Without dynamic prioritization, teams are constantly playing defense, reacting to incidents instead of stopping attacks before they happen.
- Engineers Waste Time on Redundant Work
Security engineers often analyze the same threats multiple times simply because there’s no centralized visibility.
- 40% of security teams report duplicate research efforts, leading to wasted time.
- Detection engineers spend 30% of their time revalidating IOCs already analyzed elsewhere.
- Threat intelligence is scattered across different teams, slowing down response times.
The result? Critical detections take weeks to deploy, giving attackers a huge advantage.
- Detection Engineering is Hit-or-Miss
Even when teams identify a threat, they often don’t know if they can detect it.
- 43% of engineers spend hours searching for logs, verifying whether telemetry exists for detection.
- 29% of detections fail because of missing log sources or incomplete coverage.
- Teams are forced to build detections unquestioningly, hoping their logic aligns with real-world attack behaviors.
This is why security research needs an overhaul.
Security Research Needs a Fix and Automation is the Answer
ATI automates threat research, prioritization, and detection validation, instantly eliminating wasted effort and giving security teams the insights they need. How ATI works:
Automated Processing of Threat Intelligence
To guarantee that security teams always get the most recent threat intelligence, ATI continuously examines red team reports, intelligence feeds, bug bounty results, and verified access events in real-time. ATI uses automation to quickly extract important attack information, including Indicators of Compromise (IOCs), Tactics, Techniques, and Procedures (TTPs), and severity ratings. ATI expedites the process and provides organized insights in seconds, saving analysts from having to spend hours manually examining data.
Dynamic Prioritization
Setting priorities is essential given the massive volume of threat data received every day. By considering variables like exposure levels, impacted assets, and new attack trends, ATI dynamically determines which threats represent the most danger. ATI intelligently analyzes and elevates the most urgent threats that require immediate action, preventing security teams from overloading hundreds of warnings. This guarantees that high-risk threats are never lost in the shuffle, enabling teams to react quickly and precisely.
Detection Coverage Analysis
One of the most challenging tasks in security operations is identifying whether existing detections have already addressed an identified danger. ATI automatically monitors detection coverage, avoiding repetitive research and duplicating efforts. If a detection gap is discovered, ATI promptly alerts security engineers, allowing them to focus on building the necessary detections rather than wasting time on already addressed threats. This technique eliminates inefficiencies while increasing the effectiveness of threat detection strategies.
Faster Detection Engineering
Detecting buildings is frequently laborious and involves engineers confirming that the appropriate data and logs are present. By mapping threat behaviors to current telemetry sources, ATI easily connects with log analysis platforms to ascertain whether an attack may be detected. ATI greatly accelerates the construction of efficient security measures, giving engineers an immediate answer on detection feasibility rather than requiring them to guess or spend hours looking for pertinent records.
Cut the Noise, Save Time, and Respond Faster with ATI
With ATI, security teams move from reactive firefighting to proactive threat prevention.
- Threat research time drops from 8 hours per report to just 1 hour, a 90% efficiency gain.
- Security teams save 500+ engineering hours annually by automating manual research.
- False positives are reduced by up to 40%, cutting noise and improving detection accuracy.
- High-risk threats are identified in real-time, accelerating response times by 5x.
This means faster detections, better coverage, and fewer blind spots.
The Security Model is Broken and It’s Time for a Radical Fix
The battle against cyberattacks isn’t going away. Attackers are moving more quickly and evading conventional defenses with automation, artificial intelligence, and large-scale attacks. You have to move at least as fast as they are if you want to stay up. The subsequent development is Adaptive Threat Intelligence (ATI). It ensures that security teams concentrate on actual risks rather than noise by bringing speed, automation, and intelligence to threat analysis. Teams can quickly understand what matters, what is covered, and what requires action without manually sorting through data. It is not only inefficient to do things in the old-fashioned manner. It’s a liability. Threat study needs to be reconsidered. The time has come to automate.
About the Author
Emmanuel Joshua is a Software Development Engineer (DefSec) Amazon, specializing in cybersecurity automation, threat intelligence, and detection engineering. With a background in developing scalable security solutions, Emmanuel focuses on improving threat research efficiency, detection coverage, and security operations through automation and AI-driven intelligence. His experience includes working with threat intelligence tools, data analytics, and automation frameworks to help security teams respond to developing threats in real-time. Emmanuel is passionate about streamlining security operations and minimizing research bottlenecks. He is committed to developing solutions that enable security professionals to remain ahead of new cyber threats. Emmanuel can be reached online @iamemmanueljoshua at https://www.linkedin.com/in/iamemmanueljoshua/