The startup’s platform leverages AI to automate forensic investigations, accelerating incident response.

The post Asymmetric Security Emerges From Stealth With $4.2 Million in Funding appeared first on SecurityWeek.

When I look back on my years as a SOC lead in MDR, the thing I remember most clearly is the tension between wanting to do things the “right way” and simply trying to survive the day.

The alert queue never stopped growing. The attack surface kept expanding into cloud, identity, SaaS, and whatever new platform the business adopted. And every shift ended with the same uneasy feeling: What did we miss because there wasn’t enough time to investigate everything fully?

While different sources emphasize different challenges, recent statistics from late 2024 and 2025 reports reflect exactly what so many SOC analysts and leads feel:

• The majority of alerts are never touched. Recent surveys indicate that 62% of alerts are ignored largely because the sheer volume makes them impossible to address. Furthermore, many analysts report being unable to deal with up to 67% of the daily alerts they receive.
• The volume is unmanageable for humans. A typical SOC now processes an average of 3,832 alerts per day. For analysts trying to manually triage this flood, the math simply doesn’t add up.
• Burnout is the new normal. The pressure is unsustainable, with 71% of SOC analysts reporting burnout due to alert fatigue. This has accelerated turnover, with some SOCs seeing analyst retention cycles shrink to less than 18 months, eroding institutional knowledge.

When people outside the SOC see these numbers, they assume analysts aren’t doing their jobs. The truth is the opposite. Most analysts are doing the best work they can inside a system that was never built for volume. Traditional triage is reactive and heavily dependent on intuition. On a good day, that might work. On a bad day, it leads to inconsistent decisions, coverage gaps, and immense pressure on analysts who care deeply about getting it right.

This is where the OSCAR methodology becomes valuable again.

Why the OSCAR methodology still matters

As a SOC lead, I always wanted the team to approach alerts with organizational structure. OSCAR provides that structure by creating a clear, repeatable sequence:

• Obtain Information
• Strategize
• Collect Evidence
• Analyze
• Report

It removes guesswork and helps analysts who are still developing their skills stay grounded during chaotic shifts. But here is the reality I learned firsthand – You can only scale OSCAR so far with humans alone.

Evidence collection takes time. Deep analysis takes more time. No matter how motivated an analyst is, there are simply not enough hours in a shift to apply OSCAR to every alert manually. Most teams end up applying the methodology selectively; critical and high-severity alerts get the full OSCAR treatment, while everything else gets whatever time is left.

That gap between process and reality is exactly where Intezer enters the picture.

How Intezer operationalizes OSCAR at scale

Intezer takes the proven structure of OSCAR and executes it automatically and consistently across every alert. Instead of relying on how much energy an analyst has left 45 minutes before there shift ends, Intezer performs evidence collection, deep forensic analysis, and reporting at a speed and depth no human team could sustain.

Here is how the platform automates the methodology step-by-step:

O: Information obtained

In my SOC days, gathering context meant jumping between consoles and browser tabs, hoping nothing crashed. Intezer collects all of this instantly from endpoints, cloud platforms, identity systems, and threat intel sources. Analysts start every case with the full picture rather than a partial one.

S: Strategy suggested

Instead of relying on an analyst’s instinct about what might be happening, the Intezer platform generates verdicts and risk-based priorities immediately (with 98% accuracy). This provides critical consistency, especially for junior analysts who are still finding their confidence. Additionally, all AI reasoning is fully backed by deterministic, evidence based analysis.

C: Evidence collected

This was always the slowest part of manual investigation. Intezer collects memory artifacts, files, process information, and cloud activity in seconds. No hunting, no guessing, and no hoping you pulled the right logs before they rolled over.

A: Analysis (forensic-grade)

Intezer performs genetic code analysis, behavioral analysis, static/dynamic analysis, and threat intelligence correlation on every single alert. This is the level of scrutiny senior analysts wish they had time to do manually, but usually can only afford for the most critical incidents.

Read more about how Intezer Forensic AI SOC operates under the hood.

R: Reporting & transparency

The platform creates clear, structured, audit trails. This removes the burden of manual documentation from analysts and ensures that the “why” behind every decision is transparent and explainable.

The result: Moving beyond “speed vs. depth”

When OSCAR is coupled with Intezer’s AI Forensic SOC, the operation transforms. We see this in actual customer environments:

• 100% alert coverage: Even low-severity and “noisy” alerts are fully triaged.
• Sub-minute triage: Drastically improved MTTR/MTTD and minimized backlogs.
• 98% accurate decisioning: Verdicts are supported by deterministic evidence, reducing escalations for human review to less than 4%.

The shift in operations:

Capability	Traditional MDR SOC	Intezer Forensic AI SOC
Coverage	Critical and High-severity	100% of alerts
Triage time	20+ mins per alert	<2 mins (automated)
Analyst mode	Data collector	Investigator

From the perspective of a former SOC lead, the most important benefit is this: 

”Analysts finally get to think again. Automation handles the busy work. Humans get to use judgment, creativity, and experience.”

Final thoughts

For years, triage has been treated like a speed exercise. But the threats we face today require depth, context, and clarity. OSCAR gives SOCs the investigative structure they need, and Intezer provides the scale required to actually use that structure across every alert.

For the first time, teams don’t have to choose between speed and depth. They get both.

If your SOC wants to move from reactive to truly investigative operations, we would be happy to show you what an OSCAR-driven Intezer SOC looks like in practice.

The post How AI brings the OSCAR methodology to life in the SOC appeared first on Intezer.

A judge has ruled that the plaintiffs failed to demonstrate intent to defraud investors. 

The post Investor Lawsuit Over CrowdStrike Outage Dismissed appeared first on SecurityWeek.

What happens when you ditch the tiered ticket queues and replace them with collaboration, agility, and real-time response? In this interview, Hayden Covington takes us behind the scenes of the BHIS Security Operations Center, which is where analysts don’t escalate tickets, they solve them.

The post Inside the BHIS SOC: A Conversation with Hayden Covington  appeared first on Black Hills Information Security, Inc..

1. Introduction: The Benchmark, Not the Hype

For a while now, the security community has been aware that threat actors are using AI. We’ve seen evidence of it for everything from generating phishing content to optimizing malware. The recent report from Anthropic on an “AI-orchestrated cyber espionage campaign”, however, marks a significant milestone.

This is the first time we have a public, detailed report of a campaign where AI was used at this scale and with this level of sophistication, moving the threat from a collection of AI-assisted tasks to a largely autonomous, orchestrated operation.

This report is a significant new benchmark for our industry. It’s not a reason to panic – it’s a reason to prepare. It provides the first detailed case study of a state-sponsored attack with three critical distinctions:

• It was “agentic”: This wasn’t just an attacker using AI for help. This was an AI system executing 80-90% of the attack largely on its own.
• It targeted high-value entities: The campaign was aimed at approximately 30 major technology corporations, financial institutions, and government agencies.
• It had successful intrusions: Anthropic confirmed the campaign resulted in “a handful of successful intrusions” and obtained access to “confirmed high-value targets for intelligence collection”.

Together, these distinctions show why this case matters. A high-level, autonomous, and successful AI-driven attack is no longer a future theory. It is a documented, current-day reality.

2. What Actually Happened: A Summary of the Attack

For those who haven’t read the full report (or the summary blog post), here are the key facts.

The attack (designated GTG-1002) was a “highly sophisticated cyber espionage operation” detected in mid-September 2025.

• AI Autonomy: The attacker used Anthropic’s Claude Code as an autonomous agent, which independently executed 80-90% of all tactical work.
• Human Role: Human operators acted as “strategic supervisors”. They set the initial targets and authorized critical decisions, like escalating to active exploitation or approving final data exfiltration.
• Bypassing Safeguards: The operators bypassed AI safety controls using simple “social engineering”. The report notes, “The key was role-play: the human operators claimed that they were employees of legitimate cybersecurity firms and convinced Claude that it was being used in defensive cybersecurity testing”.
• Full Lifecycle: The AI autonomously executed the entire attack chain: reconnaissance, vulnerability discovery, exploitation, lateral movement, credential harvesting, and data collection.
• Timeline: After detecting the activity, Anthropic’s team launched an investigation, banned the accounts, and notified partners and affected entities over the “following ten days”.

Source: https://www.anthropic.com/news/disrupting-AI-espionage

3. What Was Not New (And Why It Matters)

To have a credible discussion, we must also look at what wasn’t new. This attack wasn’t about secret, magical weapons.

The report is clear that the attack’s sophistication came from orchestration, not novelty.

• No Zero-Days: The report does not mention the use of novel zero-day exploits.
• Commodity Tools: The report states, “The operational infrastructure relied overwhelmingly on open source penetration testing tools rather than custom malware development”.

This matters because defenders often look for new exploit types or malware indicators. But the shift here is operational, not technical. The attackers didn’t invent a new weapon, they built a far more effective way to use the ones we already know.

4. The New Reality: Why This Is an Evolving Threat

So, if the tools aren’t new, what is? The execution model. And we must assume this new model is here to stay.

This new attack method is a natural evolution of technology. We should not expect it to be “stopped” at the source for two main reasons:

1. Commercial Safeguards are Limited: AI vendors like Anthropic are building strong safety controls – it’s how this was detected in the first place. But as the report notes, malicious actors are continually trying to find ways around them. No vendor can be expected to block 100% of all malicious activity.
2. The Open-Source Factor: This is the larger trend. Attackers don’t need to use a commercial, monitored service. With powerful open-source AI models and orchestration frameworks – such as LLaMA, self-hosted inference stacks, and LangChain/LangGraph agents – attackers can build private AI systems on their own infrastructure. This leaves no vendor in the middle to monitor or prevent the abuse.

The attack surface is not necessarily growing, but the attacker’s execution engine is accelerating.

5. Detection: Key Patterns to Hunt For

While the techniques were familiar, their execution creates a different kind of detection challenge. An AI-driven attack doesn’t generate one “smoking gun” alert, like a unique malware hash or a known-bad IP. Instead, it generates a storm of low-fidelity signals. The key is to hunt for the patterns within this noise:

• Anomalous Request Volumes: The AI operated at “physically impossible request rates” with “peak activity included thousands of requests, representing sustained request rates of multiple operations per second”. This is a classic low-fidelity, high-volume signal that is often just seen as noise.
• Commodity and Open-Source Penetration Testing Tools: The attack utilized a combination of “standard security utilities” and “open source penetration testing tools”.
• Traffic from Browser Automation: The report explicitly calls out “Browser automation for web application reconnaissance” to “systematically catalog target infrastructure” and “analyze authentication mechanisms”.
• Automated Stolen Credential Testing: The AI didn’t just test one password, it “systematically tested authentication against internal APIs, database systems, container registries, and logging infrastructure”. This automated, broad, and rapid testing looks very different from a human’s manual attempts.
• Audit for Unauthorized Account Creation: This is a critical, high-confidence post-exploitation signal. In one successful compromise, the AI’s autonomous actions included the creation of a “persistent backdoor user”.

6. The Defender’s Challenge: A Flood of Low-Fidelity Noise

The detection patterns listed above create the central challenge of defending against AI-orchestrated attacks. The problem isn’t just alert volume, it’s that these attacks generate a massive volume of low-fidelity alerts.

This new execution model creates critical blind spots:

1. The Volume Blind Spot: The AI’s automated nature creates a flood of low-confidence alerts. No human-only SOC can manually triage this volume.
2. The Temporal (Speed) Blind Spot: A human-led intrusion might take days or weeks. Here, the AI compressed a full database extraction – from authentication to data parsing – into just 2-6 hours. Our human-based detection and response loops are often too slow to keep up.
3. The Context Blind Spot: The AI’s real power is connecting many small, seemingly unrelated signals (a scan, a login failure, a data query) into a single, coherent attack chain. A human analyst, looking at these alerts one by one, would likely miss the larger pattern.

7. The Importance of Autonomous Triage and Investigation

When the attack is autonomous, the defense must also have autonomous capabilities.

We cannot hire our way out of this speed and scale problem. The security operations model must shift. The goal of autonomous triage is not just to add context, but to handle the entire investigation process for every single alert, especially the thousands of low-severity signals that AI-driven attacks create.

An autonomous system can automatically investigate these signals at machine speed, determine which ones are irrelevant noise, and suppress them.

This is the true value: the system escalates only the high-confidence, confirmed incidents that actually matter. This frees your human analysts from chasing noise and allows them to focus on real, complex threats.

This is exactly the type of challenge autonomous triage systems like the one we’ve built at Intezer were designed to solve. As Anthropic’s own report concludes, “Security teams should experiment with applying AI for defense in areas like SOC automation, threat detection… and incident response“.

8. Evolving Your Offensive Security Program

To defend against this threat, we must be able to test our defenses against it. All offensive security activities, internal red teams, external penetration tests, and attack simulations, must evolve.

It is no longer enough for offensive security teams to manually simulate attacks. To truly test your defenses, your red teams or external pentesters must adopt agentic AI frameworks themselves.

The new mandate is to simulate the speed, scale, and orchestration of an AI-driven attack, similar to the one detailed in the Anthropic report. Only then can you validate whether your defensive systems and automated processes can withstand this new class of automated onslaught. Naturally, all such simulations must be done safely and ethically to prevent any real-world risk.

9. Conclusion: When the Threat Model Changes, Our Processes Must, Too.

The Anthropic report doesn’t introduce a new magic exploit. It introduces a new execution model that we now need to design our defenses around.

Let’s summarize the key, practical takeaways:

• AI-orchestrated attacks are a proven, documented reality.
• The primary threat is speed and scale, which is designed to overwhelm manual security processes.
• Security leaders must prioritize automating investigation and triage to suppress the noise and escalate what matters.
• We must evolve offensive security testing to simulate this new class of autonomous threat.

This report is a clear signal. The threat model has officially changed. Your security architecture, processes, and playbooks must change with it. The same applies if you rely on an MSSP, verify they’re evolving their detection and triage capabilities for this new model. This shift isn’t hype, it’s a practical change in execution speed. With the right adjustments and automation, defenders can meet this challenge.

To learn more, you can read the Anthropic blog post here and the full technical report here.

The post What the Anthropic report on AI espionage means for security leaders appeared first on Intezer.

But what if we need to wrangle Windows Event Logs for more than one system? In part 2, we’ll wrangle EVTX logs at scale by incorporating Hayabusa and SOF-ELK into my rapid endpoint investigation workflow (“REIW”)! 

The post Wrangling Windows Event Logs with Hayabusa & SOF-ELK (Part 2) appeared first on Black Hills Information Security, Inc..

In part 1 of this post, we’ll discuss how Hayabusa and “Security Operations and Forensics ELK” (SOF-ELK) can help us wrangle EVTX files (Windows Event Log files) for maximum effect during a Windows endpoint investigation!

The post Wrangling Windows Event Logs with Hayabusa & SOF-ELK (Part 1) appeared first on Black Hills Information Security, Inc..

Remember the good ‘ol days of Zip drives, Winamp, the advent of “Office 365,” and copy machines that didn’t understand email authentication? Okay, maybe they weren’t so good! For a […]

The post Stop Spoofing Yourself! Disabling M365 Direct Send appeared first on Black Hills Information Security, Inc..

In this video, John Strand discusses the complexities and challenges of penetration testing, emphasizing that it goes beyond just finding and exploiting vulnerabilities.

The post 5 Things We Are Going to Continue to Ignore in 2025 appeared first on Black Hills Information Security, Inc..

Changes to the msds-KeyCredentialLink attribute are not audited/logged with standard audit configurations. This required serious investigations and a partner firm in infosec provided us the answer: TrustedSec.  So, credit where […]

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Recently in the SOC, we were notified by a partner that they had a potential business email compromise, or BEC. We commonly catch these by identifying suspicious email forwarding rules, […]

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While social engineering attacks such as phishing are a great way to gain a foothold in a target environment, direct attacks against externally exploitable services are continuing to make headlines. […]

The post In Through the Front Door – Protecting Your Perimeter   appeared first on Black Hills Information Security, Inc..

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Patterson Cake // PART 1 PART 2 In part one of “Wrangling the M365 UAL,” we talked about acquiring, parsing, and querying UAL data using PowerShell and SOF-ELK. In part […]

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The post Why Do Car Dealers Need Cybersecurity Services?  appeared first on Black Hills Information Security, Inc..

rvrsh3ll //  Introduction  This blog post is intended to give a light overview of device codes, access tokens, and refresh tokens. Here, I focus on the technical how-to for standing […]

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