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.

You read the “AI-ready SOC pillars” blog, but you still see a lot of this:

How do we do better?

Let’s go through all 5 pillars aka readiness dimensions and see what we can actually do to make your SOC AI-ready.

#1 SOC Data Foundations

As I said before, this one is my absolute favorite and is at the center of most “AI in SOC” (as you recall, I want AI in my SOC, but I dislike the “AI SOC” concept) successes (if done well) and failures (if not done at all).

Reminder: pillar #1 is “security context and data are available and can be queried by machines (API, Model Context Protocol (MCP), etc) in a scalable and reliable manner.” Put simply, for the AI to work for you, it needs your data. As our friends say here, “Context engineering focuses on what information the AI has available. […] For security operations, this distinction is critical. Get the context wrong, and even the most sophisticated model will arrive at inaccurate conclusions.”

Readiness check: Security context and data are available and can be queried by machines in a scalable and reliable manner. This is very easy to check, yet not easy to achieve for many types of data.

For example, “give AI access to past incidents” is very easy in theory (“ah, just give it old tickets”) yet often very hard in reality (“what tickets?” “aren’t some too sensitive?”, “wait…this ticket didn’t record what happened afterwards and it totally changed the outcome”, “well, these tickets are in another system”, etc, etc)

Steps to get ready:

• Conduct an “API or Die” data access audit to inventory critical data sources (telemetry and context) and stress-test their APIs (or other access methods) under load to ensure they can handle frequent queries from an AI agent. This is important enough to be a Part 3 blog after this one…
• Establish or refine unified, intentional data pipelines for the data you need. This may be your SIEM, this may be a separate security pipeline tool, this may be magick for all I care … but it needs to exist. I met people who use AI to parse human analyst screen videos to understand how humans access legacy data sources, and this is very cool, but perhaps not what you want in prod.
• Revamp case management to force structured data entry (e.g., categorized root causes, tagged MITRE ATT&CK techniques) instead of relying on garbled unstructured text descriptions, which provides clean training data for future AI learning. And, yes, if you have to ask: modern gen AI can understand your garbled stream of consciousness ticket description…. but what it makes of it, you will never know…

Where you arrive: your AI component, AI-powered tool or AI agent can get the data it needs nearly every time. The cases where it cannot become visible, and obvious immediately.

#2 SOC Process Framework and Maturity

Reminder: pillar #2 is “Common SOC workflows do NOT rely on human-to-human communication are essential for AI success.” As somebody called it, you need “machine-intelligible processes.”

Readiness check: SOC workflows are defined as machine-intelligible processes that can be queried programmatically, and explicit, structured handoff criteria are established for all Human-in-the-Loop (HITL) processes, clearly delineating what is handled by the agent versus the person. Examples for handoff to human may include high decision uncertainty, lack of context to make a call (see pillar #1), extra-sensitive systems, etc.

Common investigation and response workflows do not rely on ad-hoc, human-to-human communication or “tribal knowledge,” such knowledge is discovered and brought to surface.

Steps to get ready:

• Codify the “Tribal Knowledge” into APIs: Stop burying your detection logic in dusty PDFs or inside the heads of your senior analysts. You must document workflows in a structured, machine-readable format that an AI can actually query. If your context — like CMDB or asset inventory — isn’t accessible via API (BTW MCP is not magic!), your AI is essentially flying blind.
• Draw a Hard Line Between Agent and Human: Don’t let the AI “guess” its level of authority. Explicitly delegate the high-volume drudgery (log summarization, initial enrichment, IP correlation) to the agent, while keeping high-stakes “kill switches” (like shutting down production servers) firmly in human hands.
• Implement a “Grading” System for Continuous Learning: AI shouldn’t just execute tasks; it needs to go to school. Establish a feedback loop where humans actively “grade” the AI’s triage logic based on historical resolution data. This transforms the system from a static script into a living “recipe” that refines itself over time.
• Target Processes for AI-Driven Automation: Stop trying to “AI all the things.” Identify specific investigation workflows that are candidates for automation and use your historical alert triage data as a training ground to ensure the agent actually learns what “good” looks like.

Where you arrive: The “tribal knowledge” that previously drove your SOC is recorded for machine-readable workflows. Explicit, structured handoff points are established for all Human-in-the-Loop processes, and the system uses human grading to continuously refine its logic and improve its ‘recipe’ over time. This does not mean that everything is rigid; “Visio diagram or death” SOC should stay in the 1990s. Recorded and explicit beats rigid and unchanging.

#3 SOC Human Element and Skills

Reminder: pillar #3 is “Cultivating a culture of augmentation, redefining analyst roles, providing training for human-AI collaboration, and embracing a leadership mindset that accepts probabilistic outcomes.” You say “fluffy management crap”? Well, I say “ignore this and your SOC is dead.”

Readiness check: Leaders have secured formal CISO sign-off on a quantified “AI Error Budget,” defining an acceptable, measured, probabilistic error rate for autonomously closed alerts (that is definitely not zero, BTW). The team is evolving to actively review, grade, and edit AI-generated logic and detection output.

Steps to get ready:

• Implement the “AI Error Budget”: Stop pretending AI will be 100% accurate. You must secure formal CISO sign-off on a quantified “AI Error Budget” — a predefined threshold for acceptable mistakes. If an agent automates 1,000 hours of labor but has a 5% error rate, the leadership needs to acknowledge that trade-off upfront. It’s better to define “allowable failure” now than to explain a hallucination during an incident post-mortem.
• Pivot from “Robot Work” to Agent Shepherding: The traditional L1/L2 analyst role is effectively dead; long live the “Agent Supervisor.” Instead of manually sifting through logs — work that is essentially “robot work” anyway — your team must be trained to review, grade, and edit AI-generated logic. They are no longer just consumers of alerts; they are the “Editors-in-Chief” of the SOC’s intelligence.
• Rebuild the SOC Org Chart and RACI: Adding AI isn’t a “plug and play” software update; it’s an organizational redesign. You need to redefine roles: Detection Engineers become AI Logic Editors, and analysts become Supervisors. Most importantly, your RACI must clearly answer the uncomfortable question: If the AI misses a breach, is the accountability with the person who trained the model or the person who supervised the output?

Where you arrive: well, you arrive at a practical realization that you have “AI in SOC” (and not AI SOC). The tools augment people (and in some cases, do the work end to end too). No pro- (“AI SOC means all humans can go home”) or contra-AI (“it makes mistakes and this means we cannot use it”) crazies nearby.

#4 Modern SOC Technology Stack

Reminder: pillar #4 is “Modern SOC Technology Stack.” If your tools lack APIs, take them and go back to the 1990s from whence you came! Destroy your time machine when you arrive, don’t come back to 2026!

Readiness check: The security stack is modern, fast (“no multi-hour data queries”) interoperable and supports new AI capabilities to integrate seamlessly, tools can communicate without a human acting as a manual bridge and can handle agentic AI request volumes.

Steps to get ready:

• Mandate “Detection-as-Code” (DaC): This is no longer optional. To make your stack machine-readable, you must implement version control (Git), CI/CD pipelines, and automated testing for all detections. If your detection logic isn’t codified, your AI agent has nothing to interact with except a brittle GUI — and that is a recipe for failure.
• Find Your “Interoperability Ceiling” via Stress Testing: Before you go live, simulate reality. Have an agent attempt to enrich 50 alerts simultaneously to see where the pipes burst. Does your SOAR tool hit a rate limit? Does your threat intel provider cut you off? You need to find the breaking point of your tech stack’s interoperability before an actual incident does it for you.
• Decouple “Native” from “Custom” Agents: Don’t reinvent the wheel, but don’t expect a vendor’s “native” agent to understand your weird, proprietary legacy systems. Define a clear strategy: use native agents for standard tool-specific tasks, and reserve your engineering resources for custom agents designed to navigate your unique compliance requirements and internal “secret sauce.”

Where you arrive: this sounds like a perfect quote from Captain Obvious but you arrive at the SOC powered by tools that work with automation, and not with “human bridge” or “swivel chair.”

#5 SOC Metrics and Feedback Loop

Reminder: pillar #5 is “You are ready for AI if you can, after adding AI, answer the “what got better?” question. You need metrics and a feedback loop to get better.”

Readiness check: Hard baseline metrics (MTTR, MTTD, false positive rates) are established before AI deployment, and the team has a way to quantify the value and improvements resulting from AI. When things get better, you will know it.

Steps to get ready:

• Establish the “Before” Baseline and Fix the Data Slop: You cannot claim victory if you don’t know where the goalposts were to begin with. Measure your current MTTR and MTTD rigorously before the first agent is deployed. Simultaneously, force your analysts to stop treating case notes like a private diary. Standardize on structured data entry — categorized root causes and MITRE tags — so the machine has “clean fuel” to learn from rather than a collection of “fixed it” or “closed” comments.
• Build an “AI Gym” Using Your “Golden Set”: Do not throw your agents into the deep end of live production traffic on day one. Curate a “Golden Set” of your 50–100 most exemplary past incidents — the ones with flawless notes, clean data, and correct conclusions. This serves as your benchmark; if the AI can’t solve these “solved” problems correctly, it has no business touching your live environment.
• Adopt Agent-Specific KPIs for Performance Management: Traditional SOC metrics like “number of alerts closed” are insufficient for an AI-augmented team. You need to track Agent Accuracy Rate, Agent Time Savings, and Agent Uptime as religiously as you track patch latency. If your agent is hallucinating 5% of its summaries, that needs to be a visible red flag on your dashboard, not a surprise you discover during an incident post-mortem.
• Close the Loop with Continuous Tuning: Ensure triage results aren’t just filed away to die in an archive. Establish a feedback loop where the results of both human and AI investigations are automatically routed back to tune the underlying detection rules. This transforms your SOC from a static “filter” into a learning system that evolves with every alert.

Where you arrive: you have a fact-based visual that shows your SOC becoming better in ways important to your mission after you add AI (in fact, you SOC will get better even before AI but after you do the prep-work from this document)

As a result, we can hopefully get to this instead:

The path to an AI-ready SOC isn’t paved with new tools; it’s paved with better data, cleaner processes, and a fundamental shift in how we think about human-machine collaboration. If you ignore these pillars, your AI journey will be a series of expensive lessons in why “magic” isn’t a strategy.

But if you get these right? You move from a SOC that is constantly drowning in alerts to a SOC that operates truly 10X effectiveness.

P.S. Anton, you said “10X”, so how does this relate to ASO and “engineering-led” D&R? I am glad you asked. The five pillars we outlined are not just steps for AI; they are the also steps on the road to ASO (see original 2021 paper which is still “the future” for many).

ASO is the vision for a 10X transformation of the SOC, driven by an adaptive, agile, and highly automated approach to threats. The focus on codified, machine-intelligible workflows, a modern stack supporting Detection-as-Code, and reskilling analysts as “Agent Supervisors” directly supports the core of engineering-led D&R. So focusing on these five readiness dimensions, you move from a traditional operations room (lots of “O” for operations) to a scalable, engineering-centric D&R function (where “E” for engineering dominates).

So, which pillar is your SOC’s current ‘weakest link’? Let’s discuss in the comments and on socials!

Related blogs and podcasts:

• “Simple to Ask: Is Your SOC AI Ready? Not Simple to Answer!” (Part 1 to this blog)
• “Modern SecOps: What an AI-ready SOC actually means with Anton Chuvakin” video
• “A Brief Guide for Dealing with ‘Humanless SOC’ Idiots” (the classic!)
• “SOC is Not Dead Yet It May Be Reborn As Security Operations Center of Excellence” (oddly related!)
• EP236 Accelerated SIEM Journey: A SOC Leader’s Playbook for Modernization and AI
• EP242 The AI SOC: Is This The Automation We’ve Been Waiting For?
• EP252 The Agentic SOC Reality: Governing AI Agents, Data Fidelity, and Measuring Success
• EP249 Data First: What Really Makes Your SOC ‘AI Ready’?

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Beyond “Is Your SOC AI Ready?” Plan the Journey! was originally published in Anton on Security on Medium, where people are continuing the conversation by highlighting and responding to this story.

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 […]

The post Enable Auditing of Changes to msDS-KeyCredentialLink  appeared first on Black Hills Information Security, Inc..

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, […]

The post Monitoring High Risk Azure Logins  appeared first on Black Hills Information Security, Inc..

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..

Be sure to read PART 1! Metadata and a New-Fashioned Bank Robbery Let’s face it, some cases are just more interesting than others and, when you do incident response for […]

The post OSINT for Incident Response (Part 2) appeared first on Black Hills Information Security, Inc..

Being a digital forensics and incident response consultant is largely about unanswered questions. When we engage with a client, they know something bad happened or is happening, but they are […]

The post OSINT for Incident Response (Part 1) appeared first on Black Hills Information Security, Inc..

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 […]

The post Wrangling the M365 UAL with SOF-ELK and CSV Data (Part 3 of 3) appeared first on Black Hills Information Security, Inc..

Patterson Cake // When it comes to M365 audit and investigation, the “Unified Audit Log” (UAL) is your friend. It can be surly, obstinate, and wholly inadequate, but your friend […]

The post Wrangling the M365 UAL with PowerShell and SOF-ELK (Part 1 of 3) appeared first on Black Hills Information Security, Inc..

Troy Wojewoda // In honor of Shark Week1, I decided to write this blog to demonstrate various techniques I’ve found useful when analyzing network traffic with Wireshark, as well as […]

The post Welcome to Shark Week: A Guide for Getting Started with Wireshark and TShark appeared first on Black Hills Information Security, Inc..

Tom Smith // At Black Hills Information Security (BHIS), we deal with all manner of clients, public and private. Until a month or two ago, though, we’d never dealt with […]

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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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