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Introduction: The Rise of AI-Powered Security Operations
Cybersecurity teams are facing a battlefield that changes faster than human analysts can react. Modern attackers use automation, artificial intelligence, social engineering, and highly customized malware campaigns to bypass traditional defenses. As threats become more complex, security operations centers are beginning to explore a new generation of AI-powered systems known as agentic Security Operations Center (SOC) models.
Elastic Security is now testing these advanced AI security agents through evidence-based evaluations designed to measure whether they can perform real cybersecurity tasks, make reliable decisions, and execute workflows using actual security tools. At the same time, researchers continue tracking sophisticated threat operations, including campaigns linked to groups such as Midnight Blizzard, where attackers have abused everyday internet access points like hotel and conference Wi-Fi networks to steal Microsoft 365 credentials.
These developments reveal a major shift in cybersecurity. The future SOC will not rely only on human analysts reviewing alerts. Instead, it will combine human expertise with intelligent AI agents capable of investigating incidents, gathering evidence, and responding to attacks in real time.
Elastic Security Introduces Evidence-Based Testing for Agentic SOC Models
Elastic Security has begun benchmarking agentic SOC models through practical cybersecurity evaluations rather than simple AI performance tests. The goal is to determine whether AI agents can truly operate inside security environments and complete meaningful defensive tasks.
Traditional AI testing often focuses on language ability, reasoning benchmarks, or theoretical problem-solving. However, cybersecurity requires much more. A security AI must understand alerts, collect evidence, interact with security platforms, analyze attack behavior, and make decisions based on incomplete information.
Elastic’s evaluation approach focuses on real-world capabilities, including tool usage, investigation workflows, and evidence-backed conclusions.
Measuring Real Tool Usage Instead of Artificial Intelligence Demos
One of the biggest challenges with AI security systems is separating impressive demonstrations from practical ability.
An AI model may explain cybersecurity concepts perfectly but fail when placed inside an operational environment. Security teams need systems that can:
Search logs and telemetry.
Investigate suspicious activity.
Correlate multiple security signals.
Identify attacker behavior.
Recommend containment actions.
Document findings clearly.
Elastic’s testing framework focuses on whether AI agents can actually perform these actions rather than simply describe them.
This represents an important evolution from chatbot-style cybersecurity assistants toward autonomous security operators.
Evidence-Based Decisions Become Critical for AI Security Agents
A major concern surrounding AI-driven cybersecurity is trust.
Security analysts cannot rely on an AI system that produces confident but unsupported conclusions. A false positive can waste investigation time, while a false negative can allow attackers to remain hidden.
Evidence-based testing measures whether an AI agent can explain:
Why it reached a conclusion.
Which security events support its decision.
How reliable its recommendation is.
What additional investigation may be required.
This approach mirrors the way experienced security professionals work. Analysts do not simply identify threats, they build cases using digital evidence.
Blind Scoring for Attack Discovery and Migration Tasks
Elastic’s evaluation process includes blind scoring methods focused on specific cybersecurity challenges.
Two important areas include attack discovery and migration-related tasks.
Attack discovery measures whether AI agents can identify malicious behavior hidden within security data. This requires understanding attacker techniques, unusual patterns, and relationships between different events.
Migration tasks examine whether AI systems can assist organizations during security platform transitions while maintaining visibility and operational effectiveness.
Blind scoring helps reduce bias by evaluating performance without relying on subjective opinions.
Midnight Blizzard and CaptiveCrunch Highlight Modern Attack Methods
While defenders explore AI-powered security operations, attackers continue developing creative techniques.
Microsoft-linked investigations involving Midnight Blizzard, also known as APT29, have highlighted campaigns using techniques such as:
Hotel and conference Wi-Fi captive portals.
DNS manipulation.
Credential phishing.
Custom malware tools.
Microsoft 365 account theft.
These attacks demonstrate how threat actors increasingly combine technical exploitation with human-focused deception.
Attackers do not always need to break through advanced security systems directly. Sometimes they manipulate the environments where users connect and operate.
The Danger of Trusted Networks Becoming Attack Platforms
Public Wi-Fi networks have long been considered risky, but modern campaigns show how attackers can weaponize trusted environments.
A hotel or conference network creates a unique opportunity because users expect connectivity and may ignore suspicious login requests.
Threat actors can use fake captive portals to imitate legitimate authentication pages. Victims may enter corporate credentials, unknowingly sending access information directly to attackers.
Once attackers obtain Microsoft 365 accounts, they can potentially access emails, documents, internal communications, and cloud resources.
Custom Malware and Identity Theft Create Long-Term Threats
Modern cyber campaigns increasingly focus on identity rather than only malware infections.
Stealing a username and password can provide attackers with legitimate access that appears normal to security systems.
Custom malware can support these operations by:
Collecting browser data.
Monitoring user activity.
Maintaining persistence.
Extracting sensitive information.
Supporting further attacks.
This makes identity protection one of the most important priorities for modern organizations.
AI Versus AI: The Future Cybersecurity Battlefield
The cybersecurity industry is entering an era where both attackers and defenders will use artificial intelligence.
Attackers may use AI to automate phishing, create malware variants, discover weaknesses, and scale campaigns.
Defenders will need AI systems capable of analyzing enormous amounts of security data, detecting patterns, and responding faster than human teams alone.
The success of agentic SOC models will depend on reliability, transparency, and the ability to operate safely within complex environments.
Deep Analysis: Security Research Commands and Investigation Workflow
Security teams analyzing AI-driven SOC capabilities and advanced attacks can use traditional investigation methods combined with automation.
Checking suspicious network activity:
sudo tcpdump -i eth0 -nn
This command captures network traffic and helps analysts identify unusual communication patterns.
Reviewing authentication events:
grep "failed password" /var/log/auth.log
Useful for detecting suspicious login attempts.
Searching system logs:
journalctl -xe
Helps investigators review important system events.
Monitoring active connections:
netstat -tulpn
Shows running services and network connections.
Checking suspicious processes:
ps aux --sort=-%cpu
Helps identify abnormal resource usage.
Investigating DNS behavior:
dig example.com
Useful for analyzing domain resolution and potential DNS manipulation.
Searching malware indicators:
find / -type f -mtime -1 2>/dev/null
Can help locate recently modified files during incident response.
Reviewing cloud identity activity:
grep "login" security_events.log
Security teams can use identity logs to detect unauthorized access attempts.
AI-powered SOC systems will increasingly automate these processes, but human validation remains essential for high-impact decisions.
What Undercode Say:
The cybersecurity industry is moving from automated alerts toward autonomous investigation systems.
Agentic SOC technology represents a major change in how security teams operate.
Future AI security agents will not only analyze data, they will perform workflows.
The biggest challenge is not intelligence, but reliability.
Security teams need AI systems that can prove why they reached conclusions.
Evidence-based reasoning will become a requirement for enterprise AI adoption.
Blind testing is important because AI demonstrations often hide operational weaknesses.
Real cybersecurity environments contain incomplete information and unpredictable attacker behavior.
An AI model that performs well in controlled testing may struggle during real incidents.
Security automation must be measured through practical results.
The Midnight Blizzard activity demonstrates that attackers continue targeting human trust.
Public networks remain attractive attack surfaces because users naturally trust connectivity.
Captive portal attacks show that social engineering remains powerful.
Technical defenses alone cannot solve identity-based attacks.
Organizations must combine endpoint protection, identity security, and user awareness.
Microsoft 365 accounts remain valuable targets because they provide access to business ecosystems.
Cloud identity has become the new security perimeter.
Attackers increasingly prefer valid credentials over noisy malware infections.
Custom malware campaigns show that threat actors continue investing in specialized tools.
AI defenders must understand both technical and human attack methods.
The future SOC will likely include human analysts supervising AI investigators.
Fully autonomous cybersecurity operations still require strong safety controls.
Poorly designed AI agents could create new security risks.
Incorrect automated decisions could interrupt business operations.
Transparency will determine whether enterprises trust AI security platforms.
Security vendors must provide measurable proof of effectiveness.
AI evaluation frameworks will become as important as antivirus testing.
Organizations should demand evidence before deploying AI security solutions.
Threat detection speed will become a competitive advantage.
Attackers already use automation, meaning defenders cannot ignore AI.
Cybersecurity teams must prepare for machine-speed attacks.
Identity monitoring will become more important than traditional perimeter defense.
AI-powered investigation can reduce analyst workload.
Human expertise will remain necessary for complex incidents.
The best cybersecurity model will combine automation with human judgment.
Agentic SOC systems could transform incident response.
However, security teams must avoid blindly trusting AI decisions.
Continuous testing will determine whether these systems are effective.
The cybersecurity future will be defined by intelligent collaboration between humans and machines.
Organizations that adapt early will gain stronger protection against evolving threats.
✅ Elastic Security is testing agentic SOC models using evidence-based evaluation methods designed for cybersecurity workflows.
✅ AI-driven security operations are becoming a major focus as organizations search for faster threat detection and response.
✅ Midnight Blizzard campaigns have been associated with sophisticated phishing, credential theft, and cloud account targeting techniques.
Prediction
(+1) AI-powered SOC agents will become increasingly common in enterprise security environments as organizations need faster investigation and response capabilities.
(+1) Evidence-based AI testing frameworks will become a standard requirement before companies deploy autonomous cybersecurity systems.
(+1) Human analysts and AI assistants will increasingly work together, creating hybrid security teams.
(-1) Poorly controlled AI security systems may create new risks if organizations allow automated decisions without proper verification.
(-1) Attackers will continue targeting identities, cloud accounts, and trusted networks because these methods remain highly effective.
Conclusion: A New Chapter for Cyber Defense
The cybersecurity industry is entering a critical transformation period. Artificial intelligence is changing how defenders investigate attacks, while threat actors continue finding creative ways to exploit human behavior and digital trust.
Elastic’s agentic SOC evaluations represent a step toward more intelligent security operations, but success will depend on transparency, evidence, and responsible deployment.
At the same time, campaigns involving Midnight Blizzard demonstrate that organizations must continue strengthening identity protection and user awareness.
The future of cybersecurity will not be defined by humans versus machines. It will be defined by how effectively they work together against an increasingly automated threat landscape.
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