Many security leader are asking the same question right now. We already pay for Microsoft Copilot, ChatGPT Enterprise, or Claude. Why buy anything else?

It is a fair question. These are genuinely impressive platforms. And the honest answer is that they can help with some things. Just not the things that matter most for most SOC teams.

This post is a practical guide to where generalist AI earns its place in a SOC and where it runs out of road.

Where generalist AI platforms actually add value

Let’s be direct about what generalist AI platforms do well in a security context.

They are good at drafting, incident summaries, policy documentation, communication templates, and post-mortems. If an analyst needs to translate a technical finding into plain language for an executive, a general-purpose LLM can accelerate that substantially.

They are useful for on-demand research. Asking a question about a CVE, looking up MITRE ATT&CK techniques, or getting a quick primer on an unfamiliar attack class. These are real productivity wins.

They can assist with simple scripting and query construction. Writing a KQL query for a Sentinel rule, generating a Python snippet to parse a log format. Useful, time-saving work.

The common thread is that these are assistance tasks. A human still needs to initiate the process while the AI is a capable co-pilot. And for these use cases, a general-purpose tool is perfectly appropriate.

Where generalist AI runs out of road

The problem is that none of those use cases address the actual constraint facing most SOC teams.

Security teams are not failing because analysts lack knowledge or work too slowly. They are constrained by investigative capacity. Alert volumes are rising. Environments are growing. Attacks are moving faster. And the operating model still assumes humans will triage and investigate the majority of what comes in.

When that assumption breaks down, investigation becomes selective. High-severity alerts get attention. Medium alerts accumulate. Low-severity alerts are deferred or auto-closed. And the uncomfortable truth is that real attacks frequently begin as weak signals. Credential misuse, living-off-the-land techniques, early-stage lateral movement. They rarely present as critical alerts. They appear ordinary until someone actually investigates them.

Generic AI does not fix this. Here is why.

Generalist AI is built for breadth, not depth

ChatGPT and Microsoft Copilot are built for general-purpose text generation. Forensic investigation of a suspicious process execution chain, or a cloud misconfiguration alert at 3am, requires domain-specific knowledge and structured reasoning those platforms were not designed to provide.

Generalist AI assists but does not execute 

Even with a great prompt, a general-purpose AI is accelerating an analyst’s workflow, not replacing the need for one. The investigation still depends on human capacity. And human capacity does not scale as fast as the alert surface grows.

Generalist AI KPIs are increased token usage

Microsoft’s KPI, for example, is token usage. More engagement equals more revenue, regardless of whether your security outcomes improved. That is not a subtle difference. It shapes every product decision, every definition of success. And this can result in very high costs for SOC teams heavily relying on these platforms. This is in stark contrast to Intezer AI SOC which selectively uses LLMs while primarily executing forensic investigations with highly scalable tools and processes. 

Read more about how Intezer Forensic AI SOC follows Anthropic’s best practices.

A practical AI decision framework

Use generalist AI when:

• The task requires drafting or synthesizing text and security context is not critical to the output
• An analyst is researching something unfamiliar and needs a starting point
• The work is advisory and a human will validate and act on every output
• Speed of completion matters more than forensic accuracy

Consider purpose-built AI when:

• You need investigation to happen without an analyst driving every step
• Alert volume has outpaced the team’s capacity to investigate manually
• Medium and low-severity alerts are going uninvestigated because there simply is not time
• You need verdicts accurate enough to act on, not just suggestions to review

The line between these two categories comes down to one question. Do you need AI assistance, or do you need AI execution?

What autonomous execution actually requires

This distinction matters because it shapes what you need from a platform.

Assistance is achievable with a good LLM and a capable prompt. Execution requires something harder: accuracy and forensic depth at investigation time.

General-purpose AI tools and many first-generation AI SOC products rely primarily on LLM analysis and SIEM queries. That is not enough to produce verdicts you can trust without a human checking every one.

Intezer AI SOC is built for the execution side of that line. Automated evidence collection, threat intelligence correlation, network forensics, endpoint forensics, and reverse engineering. That additional depth is what generates the high-confidence verdicts that allow organizations to trust the outcome without a human reviewing every decision.

Below a certain threshold of accuracy and depth, AI assists humans. Above it, organizations can safely offload Tier 1 and Tier 2 work entirely. The threshold is not crossed through breadth. It is crossed through domain specialization and forensic rigor.

Intezer’s investigations produce evidence-based verdicts with 98% accuracy. Up to 2% of alerts are escalated as real incidents while the rest are resolved automatically. That is not a productivity improvement. That is a fundamentally different operating model.

The closed loop of triage and detection engineering

There is one more dimension where general-purpose tools fall short and that is detection engineering.

When a generic AI tool helps an analyst triage an alert, that interaction is largely isolated. The outcome does not feed back into your SIEM rules. It does not surface coverage gaps. It does not help you get better at detecting the same class of threat next time.

Intezer’s investigation outcomes feed directly into detection engineering at the source, continuously identifying broken or noisy rules, flagging coverage gaps against the MITRE ATT&CK framework, and generating deployment-ready detection rules informed by real investigation results. The system improves with every alert it processes. Detection gets better based on evidence, not assumptions.

That closed loop is the difference between a productivity tool and an operating model.

Is a single generalist interface with multiple plugins the answer?

There is also an important architectural point worth making. Generalist AI platforms are increasingly effective at consolidating workflows into a single interface, and in theory, you could extend them into security operations through plugins and MCPs. The building blocks exist.

 

But in practice, stitching together the specialist capabilities needed for real alert triage such as forensic evidence collection, threat intelligence correlation, reverse engineering, network analysis, etc.  means sourcing, integrating, and maintaining a patchwork of plugins across multiple providers. Each one has its own update cycle, its own failure modes, and its own gaps. The integration burden falls on your team, and keeping it all working reliably over time is its own operational overhead.

 

At some point the question becomes whether the effort of assembling and maintaining a DIY investigation pipeline inside a generalist platform is worth it — or whether it makes more sense to use a purpose-built system where those capabilities are already unified, tested, and working together out of the box.

The bottom line

Generalist AI platforms have a real role to play in the SOC. Use them for drafting, research, and analyst-driven assistance tasks. It is good at those things and it is likely already paid for.

But do not confuse that with solving the capacity problem. When investigation still depends on human bandwidth, the alert backlog does not disappear. It just accumulates more slowly.

The future SOC is one where AI executes investigation and humans supervise outcomes. Getting there requires technology purpose-built for that job.

Learn more about Intezer AI SOC.

The post Generalist AI for your SOC: When and where to use it appeared first on Intezer.

Introduction

The primary goal for attackers in a phishing campaign is to bypass email security and trick the potential victim into revealing their data. To achieve this, scammers employ a wide range of tactics, from redirect links to QR codes. Additionally, they heavily rely on legitimate sources for malicious email campaigns. Specifically, we’ve recently observed an uptick in phishing attacks leveraging Amazon SES.

The dangers of Amazon SES abuse

Amazon Simple Email Service (Amazon SES) is a cloud-based email platform designed for highly reliable transactional and marketing message delivery. It integrates seamlessly with other products in Amazon’s cloud ecosystem, AWS.

At first glance, it might seem like just another delivery channel for email phishing, but that isn’t the case. The insidious nature of Amazon SES attacks lies in the fact that attackers aren’t using suspicious or dangerous domains; instead, they are leveraging infrastructure that both users and security systems have grown to trust. These emails utilize SPF, DKIM, and DMARC authentication protocols, passing all standard provider checks, and almost always contain .amazonses.com in the Message-ID headers. Consequently, from a technical standpoint, every email sent via Amazon SES – even a phishing one – looks completely legitimate.

Phishing URLs can be masked with redirects: a user sees a link like amazonaws.com in the email and clicks it with confidence, only to be sent to a phishing site rather than a legitimate one. Amazon SES also allows for custom HTML templates, which attackers use to craft more convincing emails. Because this is legitimate infrastructure, the sender’s IP address won’t end up on reputation-based blocklists. Blocking it would restrict all incoming mail sent through Amazon SES. For major services, that kind of measure is ineffective, as it would significantly disrupt user workflows due to a massive number of false positives.

How compromise happens

In most cases, attackers gain access to Amazon SES through leaked IAM (AWS Identity and Access Management) access keys. Developers frequently leave these keys exposed in public GitHub repositories, ENV files, Docker images, configuration backups, or even in publicly accessible S3 buckets. To hunt for these IAM keys, phishers use various tools, such as automated bots based on the open-source utility TruffleHog, which is designed for detecting leaked secrets. After verifying the key’s permissions and email sending limits, attackers are equipped to spread a massive volume of phishing messages.

Examples of phishing with Amazon SES

In early 2026, one of the most common themes in phishing emails sent with Amazon SES was fake notifications from electronic signature services.

The email’s technical headers confirm that it was sent with Amazon SES. At first glance, it all looks legitimate enough.

In these emails, the victim is typically asked to click a link to review and sign a specific document.

Upon clicking the link, the user is directed to a sign-in form hosted on amazonaws.com. This can easily mislead the victim, convincing them that what they’re doing is safe.

The resulting form is, of course, a phishing page, and any data entered into it goes directly to the attackers.

Amazon SES and BEC

However, Amazon SES is used for more than just standard phishing; it’s also a vehicle for a very sophisticated type of BEC campaigns. In one case we investigated, a fraudulent email appeared to contain a series of messages exchanged between an employee of the target organization and a service provider about an outstanding invoice. The email was sent as if from that employee to the company’s finance department, requesting urgent payment.

The PDF attachments didn’t contain any malicious phishing URLs or QR codes, only payment details and supporting documentation.

Naturally, the email didn’t originate with the employee, but with an attacker impersonating them. The entire thread quoted within the email was actually fabricated, with the messages formatted to appear as a legitimate forwarded thread to a cursory glance. This type of attack aims to lower the user’s guard and trick them into transferring funds to the scammers’ account.

Takeaways

Phishing via Amazon SES experienced an uptick in January 2026 and has remained relatively steady through Q1. By weaponizing this service, attackers avoid the effort of building dubious domains and mail infrastructure from scratch. Instead, they hijack existing access keys to gain the ability to blast out thousands of phishing emails. These messages pass email authentication, originate from IP addresses that are unlikely to be blocklisted, and contain links to phishing forms that look entirely legitimate.

Since these Amazon SES phishing attacks stem from compromised or leaked AWS credentials, prioritizing the security of these accounts is critical. To mitigate these risks, we recommend following these guidelines:

• Implement the principle of least privilege when configuring IAM access keys, granting elevated permissions only to users who require them for specific tasks.
• Transition from IAM access keys to roles when configuring AWS; these are profiles with specific permissions that can be assigned to one or several users.
• Enable multi-factor authentication, an ever-relevant step.
• Configure IP-based access restrictions.
• Set up automated key rotation and run regular security audits.
• Use the AWS Key Management Service to encrypt data with unique cryptographic keys and manage them from a centralized location.

We recommend that users remain vigilant when handling email. Do not determine whether an email is safe based solely on the From field. If you receive unexpected documents via email, a prudent precaution is to verify the request with the sender through a different communication channel. Always carefully inspect where links in the body of an email actually lead. Additionally, robust email security solutions can provide an essential layer of protection for both corporate and personal correspondence.

Introduction

The primary goal for attackers in a phishing campaign is to bypass email security and trick the potential victim into revealing their data. To achieve this, scammers employ a wide range of tactics, from redirect links to QR codes. Additionally, they heavily rely on legitimate sources for malicious email campaigns. Specifically, we’ve recently observed an uptick in phishing attacks leveraging Amazon SES.

The dangers of Amazon SES abuse

Amazon Simple Email Service (Amazon SES) is a cloud-based email platform designed for highly reliable transactional and marketing message delivery. It integrates seamlessly with other products in Amazon’s cloud ecosystem, AWS.

At first glance, it might seem like just another delivery channel for email phishing, but that isn’t the case. The insidious nature of Amazon SES attacks lies in the fact that attackers aren’t using suspicious or dangerous domains; instead, they are leveraging infrastructure that both users and security systems have grown to trust. These emails utilize SPF, DKIM, and DMARC authentication protocols, passing all standard provider checks, and almost always contain .amazonses.com in the Message-ID headers. Consequently, from a technical standpoint, every email sent via Amazon SES – even a phishing one – looks completely legitimate.

Phishing URLs can be masked with redirects: a user sees a link like amazonaws.com in the email and clicks it with confidence, only to be sent to a phishing site rather than a legitimate one. Amazon SES also allows for custom HTML templates, which attackers use to craft more convincing emails. Because this is legitimate infrastructure, the sender’s IP address won’t end up on reputation-based blocklists. Blocking it would restrict all incoming mail sent through Amazon SES. For major services, that kind of measure is ineffective, as it would significantly disrupt user workflows due to a massive number of false positives.

How compromise happens

In most cases, attackers gain access to Amazon SES through leaked IAM (AWS Identity and Access Management) access keys. Developers frequently leave these keys exposed in public GitHub repositories, ENV files, Docker images, configuration backups, or even in publicly accessible S3 buckets. To hunt for these IAM keys, phishers use various tools, such as automated bots based on the open-source utility TruffleHog, which is designed for detecting leaked secrets. After verifying the key’s permissions and email sending limits, attackers are equipped to spread a massive volume of phishing messages.

Examples of phishing with Amazon SES

In early 2026, one of the most common themes in phishing emails sent with Amazon SES was fake notifications from electronic signature services.

The email’s technical headers confirm that it was sent with Amazon SES. At first glance, it all looks legitimate enough.

In these emails, the victim is typically asked to click a link to review and sign a specific document.

Upon clicking the link, the user is directed to a sign-in form hosted on amazonaws.com. This can easily mislead the victim, convincing them that what they’re doing is safe.

The resulting form is, of course, a phishing page, and any data entered into it goes directly to the attackers.

Amazon SES and BEC

However, Amazon SES is used for more than just standard phishing; it’s also a vehicle for a very sophisticated type of BEC campaigns. In one case we investigated, a fraudulent email appeared to contain a series of messages exchanged between an employee of the target organization and a service provider about an outstanding invoice. The email was sent as if from that employee to the company’s finance department, requesting urgent payment.

The PDF attachments didn’t contain any malicious phishing URLs or QR codes, only payment details and supporting documentation.

Naturally, the email didn’t originate with the employee, but with an attacker impersonating them. The entire thread quoted within the email was actually fabricated, with the messages formatted to appear as a legitimate forwarded thread to a cursory glance. This type of attack aims to lower the user’s guard and trick them into transferring funds to the scammers’ account.

Takeaways

Phishing via Amazon SES experienced an uptick in January 2026 and has remained relatively steady through Q1. By weaponizing this service, attackers avoid the effort of building dubious domains and mail infrastructure from scratch. Instead, they hijack existing access keys to gain the ability to blast out thousands of phishing emails. These messages pass email authentication, originate from IP addresses that are unlikely to be blocklisted, and contain links to phishing forms that look entirely legitimate.

Since these Amazon SES phishing attacks stem from compromised or leaked AWS credentials, prioritizing the security of these accounts is critical. To mitigate these risks, we recommend following these guidelines:

• Implement the principle of least privilege when configuring IAM access keys, granting elevated permissions only to users who require them for specific tasks.
• Transition from IAM access keys to roles when configuring AWS; these are profiles with specific permissions that can be assigned to one or several users.
• Enable multi-factor authentication, an ever-relevant step.
• Configure IP-based access restrictions.
• Set up automated key rotation and run regular security audits.
• Use the AWS Key Management Service to encrypt data with unique cryptographic keys and manage them from a centralized location.

We recommend that users remain vigilant when handling email. Do not determine whether an email is safe based solely on the From field. If you receive unexpected documents via email, a prudent precaution is to verify the request with the sender through a different communication channel. Always carefully inspect where links in the body of an email actually lead. Additionally, robust email security solutions can provide an essential layer of protection for both corporate and personal correspondence.

As Frontier AI crosses new thresholds, the landscape for both attackers and defenders is shifting. At Palo Alto Networks, we are committed to ensuring defenders maintain the advantage.

To deliver this critical edge, our Unit 42 Frontier AI Defense will now leverage Anthropic’s Claude Security, powered by Opus 4.7. By integrating one of the world’s most advanced AI models, we are empowering our customers to outpace automated threats. Through Frontier AI Defense, organizations can rapidly assess their security posture, remediate vulnerabilities and harden their infrastructure against next-generation, AI-driven attacks.

We are utilizing Claude Security’s deep technical reasoning to enable our customers to find and fix vulnerabilities with unprecedented speed. This includes:

1. AI-Driven Exposure Analysis – Identifying complex exploit chains that turn minor findings into critical risks.
2. Scalable Application Analysis – Performing deep-stack code reviews at a scale and depth previously unavailable.
3. Agentic Defense – Powering autonomous workflows that detect and remediate threats at machine speed, backed by human oversight.

Palo Alto Networks is also participating in Anthropic's Cyber Verification Program, which credentials security teams for legitimate defensive use of frontier models.

The threat timeline is accelerating. Within months, AI-driven attack capabilities will become a standard fixture of the threat landscape. Palo Alto Networks is dedicated to ensuring our global customers are equipped with the modern frontier AI models necessary to stay secure both today and tomorrow.

The post Enhancing AI-Driven Defense with Anthropic’s Claude Opus 4.7 appeared first on Palo Alto Networks Blog.

A researcher has discovered a weakness called PhantomRPC that Microsoft does not consider a vulnerability it plans to patch.

PhantomRPC involves Windows Remote Procedure Call (RPC), the core of communication between Windows processes. The vulnerability lets a process with impersonation rights escalate to SYSTEM by impersonating high‑privileged clients that connect to a fake RPC server.

The researcher presented a detailed technical report outlining five exploitation paths, including coercion, user interaction, or background services. They warned that potential vectors are “effectively unlimited” because the root issue is architectural.

Microsoft, however, classified the issue as “moderate,” refused a bounty, declined to assign a CVE (a spot in the list of Common Vulnerabilities and Exposures), and closed the case without tracking. Its position is that the technique requires an already‑compromised machine and does not provide unauthenticated or remote access.

Experts disagreed with Microsoft’s assessment. Their concern is that Microsoft is downplaying a systemic local privilege escalation technique that exists in all supported Windows versions.

The issue

At the core of this issue is that the Windows RPC runtime does not sufficiently verify that the server a high‑privileged client connects to is the intended legitimate endpoint.

If a legitimate RPC server is not reachable (for example because the service stopped, was misconfigured, not installed, or due to a race condition), an attacker with SeImpersonatePrivilege can spin up a fake RPC server that “fills the gap” using the same interface and endpoint.

When a SYSTEM or high‑privileged client connects to this fake server, using an impersonation level that allows the server to impersonate the client, the attacker can call RpcImpersonateClient and immediately escalate their privileges to SYSTEM.

From Microsoft’s perspective, the ability to run a rogue RPC server in this way falls under the category of “already compromised.”

SeImpersonatePrivilege

To understand the issue better, we need to dig into what SeImpersonatePrivilege does.

Basically, SeImpersonatePrivilege is the Windows permission that lets a program “pretend to be you” after you’ve already logged in, so it can do things on your behalf using your level of access.

It’s needed because many system services and server‑type apps (file sharing, RPC servers, COM servers, web apps) have to perform actions on behalf of a user, like reading their files or applying group policy.

If an attacker gains this privilege, they can create a fake service or server and wait for a more powerful account to talk to it. When that high‑privilege service connects, the attacker can grab its security token and impersonate it, effectively upgrading from an account with lower privileges to full SYSTEM control on that machine.

Protection

A Microsoft spokesperson provided the following statement:

“This technique requires an already-compromised machine and does not grant unauthenticated or remote access. Any update is a balance between existing compatibility and customer risk, and we remain committed to continually hardening our products. We recommend customers follow security best practices, including limiting administrative privileges and applying the principle of least privilege.”

In our opinion, mitigating PhantomRPC properly would require deep changes to the RPC architecture, which is hard to do on existing Windows versions without breaking compatibility. It’s maybe something we’ll see in future versions, given the scale of change needed.

What you can do:

• As PhantomRPC is a piece in a larger chain, it is still very important to keep Windows updated.
• Use your admin account sparingly and only for the tasks that need that kind of privilege.
• Use an up-to-date, real-time anti-malware solution that can detect and block suspicious privilege‑escalation activity.
• Avoid disabling or “hardening” services blindly since a malicious service might step in their place.

To answer the question in the title: it looks like a “feature” that can be abused in many ways; one that has outlived its original threat model. Defenders have to treat them as ongoing risks, rather than one‑off CVEs.

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A researcher has discovered a weakness called PhantomRPC that Microsoft does not consider a vulnerability it plans to patch.

PhantomRPC involves Windows Remote Procedure Call (RPC), the core of communication between Windows processes. The vulnerability lets a process with impersonation rights escalate to SYSTEM by impersonating high‑privileged clients that connect to a fake RPC server.

The researcher presented a detailed technical report outlining five exploitation paths, including coercion, user interaction, or background services. They warned that potential vectors are “effectively unlimited” because the root issue is architectural.

Microsoft, however, classified the issue as “moderate,” refused a bounty, declined to assign a CVE (a spot in the list of Common Vulnerabilities and Exposures), and closed the case without tracking. Its position is that the technique requires an already‑compromised machine and does not provide unauthenticated or remote access.

Experts disagreed with Microsoft’s assessment. Their concern is that Microsoft is downplaying a systemic local privilege escalation technique that exists in all supported Windows versions.

The issue

At the core of this issue is that the Windows RPC runtime does not sufficiently verify that the server a high‑privileged client connects to is the intended legitimate endpoint.

If a legitimate RPC server is not reachable (for example because the service stopped, was misconfigured, not installed, or due to a race condition), an attacker with SeImpersonatePrivilege can spin up a fake RPC server that “fills the gap” using the same interface and endpoint.

When a SYSTEM or high‑privileged client connects to this fake server, using an impersonation level that allows the server to impersonate the client, the attacker can call RpcImpersonateClient and immediately escalate their privileges to SYSTEM.

From Microsoft’s perspective, the ability to run a rogue RPC server in this way falls under the category of “already compromised.”

SeImpersonatePrivilege

To understand the issue better, we need to dig into what SeImpersonatePrivilege does.

Basically, SeImpersonatePrivilege is the Windows permission that lets a program “pretend to be you” after you’ve already logged in, so it can do things on your behalf using your level of access.

It’s needed because many system services and server‑type apps (file sharing, RPC servers, COM servers, web apps) have to perform actions on behalf of a user, like reading their files or applying group policy.

If an attacker gains this privilege, they can create a fake service or server and wait for a more powerful account to talk to it. When that high‑privilege service connects, the attacker can grab its security token and impersonate it, effectively upgrading from an account with lower privileges to full SYSTEM control on that machine.

Protection

A Microsoft spokesperson provided the following statement:

“This technique requires an already-compromised machine and does not grant unauthenticated or remote access. Any update is a balance between existing compatibility and customer risk, and we remain committed to continually hardening our products. We recommend customers follow security best practices, including limiting administrative privileges and applying the principle of least privilege.”

In our opinion, mitigating PhantomRPC properly would require deep changes to the RPC architecture, which is hard to do on existing Windows versions without breaking compatibility. It’s maybe something we’ll see in future versions, given the scale of change needed.

What you can do:

• As PhantomRPC is a piece in a larger chain, it is still very important to keep Windows updated.
• Use your admin account sparingly and only for the tasks that need that kind of privilege.
• Use an up-to-date, real-time anti-malware solution that can detect and block suspicious privilege‑escalation activity.
• Avoid disabling or “hardening” services blindly since a malicious service might step in their place.

To answer the question in the title: it looks like a “feature” that can be abused in many ways; one that has outlived its original threat model. Defenders have to treat them as ongoing risks, rather than one‑off CVEs.

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