Discover how Kimwolf v7 targets Android IoT devices with HTTP/2 DDoS fingerprinting, Ethereum ENS C2 resolution and Tor backup routing.
The post Kimwolf v7: An Evolution of the Kimwolf Botnet appeared first on Unit 42.
Analysis of the Aeternum botnet loader, a threat leveraging Polygon blockchain smart contracts for decentralized C2 infrastructure and payload execution.
The post The Permanent Threat: Analyzing Aeternum’s Blockchain-Based C2 Operations and Communications appeared first on Unit 42.
Identity-based attacks drive 90% of incidents. Learn how modern attackers exploit identities and what SOC leaders can do to respond.
The post Inside the Modern SOC: The Identity Front Door appeared first on Unit 42.
Nearly half of C2 malware bypasses DNS by connecting directly to IP addresses. Zero trust IP enforcement secures networks against these threats.
The post Almost Half of Malware Samples Communicate Direct to IP appeared first on Unit 42.
Amazon Web Services (AWS) is pleased to announce that the Summer 2026 System and Organization Controls (SOC) 1 report is now available. The reports cover 185 services over the 12-month period from July 1, 2025–June 30, 2026, giving customers a full year of assurance. These reports demonstrate our continuous commitment to adhering to the heightened expectations of cloud service providers.
Customers can download the Summer 2026 SOC 1 report through AWS Artifact, a self-service portal for on-demand access to AWS compliance reports. Sign in to AWS Artifact in the AWS Management Console, or learn more at Getting Started with AWS Artifact.
AWS strives to continuously bring services into the scope of its compliance programs to help customers meet their architectural and regulatory needs. You can view the current list of services in scope on our Services in Scope page. As an AWS customer, you can reach out to your AWS account team if you have any questions or feedback about SOC compliance.
To learn more about AWS compliance and security programs, see AWS Compliance Programs.
The US Federal Trade Commission (FTC), together with Utah and California, has filed a lawsuit against telehealth provider Hims & Hers.
The FTC alleges that the company shared consumers’ sensitive health information with third‑party advertising platforms despite promising strong privacy protections.
Hims & Hers is a telehealth and digital health platform that connects users with licensed medical providers for online consultations, prescription medications, and personal care products.
The complaint also accuses Hims & Hers of deceptive billing and subscription practices that made it hard for users to avoid charges or cancel subscriptions.
According to the FTC’s complaint, filed in federal court in California, Hims & Hers:
From a cybersecurity and privacy research perspective, this isn’t just about a single telehealth brand. It highlights three broader trends we see repeatedly in consumer programs:
Privacy policies versus reality. A company can market itself as privacy‑focused while still integrating third‑party advertising and analytics software development kits (SDKs) that leak sensitive information. This becomes especially concerning when health‑related events are linked to user accounts or tracking cookies.
Friction as a feature. Hard‑to‑find cancellation flows and unclear billing practices are examples of “dark patterns” that nudge users into paying for services they might not have chosen given all relevant information.
Regulatory pressure is growing. Health‑related services are under increasing scrutiny, especially when they handle sensitive data and combine it with advertising platforms.
The court will ultimately decide whether Hims & Hers violated the law, but the FTC’s action sends a clear signal: regulators are paying close attention to how health‑related services collect, use, and share sensitive data.
For anyone who values online privacy, the Hims & Hers case is a reminder that “health tech” does not automatically mean “privacy first.”
More often than not, the privacy loopholes are hidden in the privacy policy somewhere.
Pro tip: one thing AI is good at is reading between the lines. Ask an AI chatbot to summarize a privacy policy and identify when your information may be shared with third parties. AI makes it much easier to understand lengthy privacy policies without reading every word yourself. If companies fail to follow their own privacy policies, regulators and consumers can hold them accountable.
Other than that:
Your name, address, and phone number may already be for sale.
Data brokers collect and sell your personal details to anyone willing to pay. Malwarebytes Personal Data Remover finds them and gets your information removed, then keeps watch so it stays that way.
Leveraging legitimate software is one of cybercriminals’ tactics of choice, with remote management tools ranking among their top tools. A recent example involves the remote administration utility ScreenConnect. It’s designed for IT support teams to troubleshoot systems and configure software seamlessly in the background. However, when weaponized by threat actors, ScreenConnect becomes a versatile attack vehicle used to harvest data, deploy malware, and move laterally across corporate networks.
During a recent incident detected by Kaspersky Managed Detection and Response, our experts identified an attempt to use ScreenConnect in an attack. This allowed a detailed study of how attackers used this application in a large-scale malware distribution campaign. The following breakdown illustrates the mechanics of ScreenConnect-assisted attacks, and outlines key strategies to defend your organization against them.
In the campaign analyzed by our experts, the attackers bundled ScreenConnect with legitimate free business software. They established a network of phishing websites to spoof popular tools, including OBS Studio, DS4Windows, DNS Jumper, Glary Utilities, Bandizip, Process Hacker, and others.
These rogue websites featured high-quality designs that could be taken for the official pages, making them highly convincing to unsuspecting users. Once the victim clicks the download button for the software, an archive is downloaded to their computer that contains additional files alongside the requested application:
Launching the renamed file disguised as the app installer triggers DLL sideloading of a malicious library. This library silently runs the ScreenConnect installation without restarting the system, while using the standard Windows installer to set up the software the user originally tried to install.
The attackers used search engine optimization techniques to drive traffic to their fake websites. As a result, these malicious pages appeared at the top of search results for certain free software utilities on major search engines.
Our experts discovered over 90 domain names translated into more than 10 different languages. While most of these websites targeted English, Russian, and Chinese speakers, several domains catered to German, French, Spanish, Arabic, and other regional audiences.
A detailed analysis of the IP addresses and associated spoofed domains is available in our technical research article on Securelist, along with full indicators of compromise.
In this campaign, attackers leveraged ScreenConnect to generate and execute malicious scripts on victim machines. These scripts served several key functions: they created exclusions for specific drives, directories, and processes within Windows Defender, disabled the User Account Control (UAC) security mechanism, and delivered and deployed AsyncRAT – a remote access Trojan.
To maintain persistence, the scripts configured a Windows scheduled task to run the malicious code at preset intervals. AsyncRAT then established a connection with the attackers’ command-and-control server to receive further instructions.
The primary objective of this campaign appears to be gaining unauthorized access to enterprise systems, likely to then resell it on cybercrime marketplaces.
Although ScreenConnect in and of itself is a legitimate tool, its presence poses a security risk to corporate environments. Consequently, Kaspersky security solutions detect this application as not-a-virus:HEUR:RemoteAdmin.MSIL.ConnectWise.gen.
Security teams should implement the following controls:
As noted previously, this campaign was originally detected through the Kaspersky Managed Detection and Response service, which can be employed to protect against such threats.
by Dan “Haircutfish” Rearden | haircutfish.com | Guest Author Working in the SOC can be a grind. Whether triaging alerts, escalating to clients, or just trying to understand why users […]
The post Report As You Go: Maintaining Good Documentation for SOC Analysts appeared first on Black Hills Information Security, Inc..
When an administrator introduces a rule change in AWS Network Firewall and network connectivity is disrupted, pinpointing the cause requires inspecting multiple points in the traffic path. The firewall gives you stateless and stateful rule engines, domain rules, and routing to the firewall endpoint inside your Amazon Virtual Private Cloud (Amazon VPC). A network drop looks the same from the workload no matter where it started. Isolating the cause means correlating the alert and flow logs with the firewall configuration, route tables, and recent API calls in AWS CloudTrail that might have changed them. That manual correlation is exactly where AWS DevOps Agent helps, accelerating root cause analysis so you can restore connectivity in minutes instead of hours.
AWS DevOps Agent does that correlation for you. As your always-available operations teammate, it resolves and proactively prevents operational issues across AWS, multicloud, and on-premises environments. When an Amazon CloudWatch alarm triggers, it reaches the agent through a webhook. The agent then reads the firewall configuration and logs through AWS APIs, ties the drop to recent API activity, and returns a root cause with a mitigation plan you review before you apply it.
This post connects CloudWatch monitoring to DevOps Agent. It walks through three Network Firewall failures from end to end. The first is a domain deny list blocking a legitimate endpoint. The second is a stateless rule priority misconfiguration. The third is an asymmetric cross Availability Zone (AZ) routing drop. Each maps to a different layer, so each leads down a different investigation path. An AWS Cloud Development Kit (AWS CDK) app deploys the whole environment in your own account so you can reproduce each failure and follow along.
As part of this blog post, we provide a CDK stack that deploys both the AWS DevOps Agent Space and a sample workload used to walk through three separate troubleshooting scenarios. A single t3.micro instance in a protected subnet checks its connectivity to a test endpoint on a continuous loop and publishes results to CloudWatch. Traffic takes the internet egress path through Network Firewall, the NAT gateway, and the internet gateway, so the firewall can intercept or drop it. After completing the walkthrough, you can apply the same troubleshooting techniques with DevOps Agent against your own Network Firewall deployments.
The test endpoint runs in a separate VPC deployed by the same CDK app. It serves HTTPS on port 443 and TCP on port 9142, giving each scenario a different protocol layer to exercise: Scenario 1 targets a TLS connection on 443 (matched by Server Name Indication), Scenario 2 targets a TCP connection on 9142, and Scenario 3 exercises the whole egress path.
A live status page shows one card per scenario plus the network topology. The whole stack deploys from a single CDK app across two Availability Zones, each with a firewall endpoint and NAT gateway, which is what makes Scenario 3 possible.
As shown in the following figure, the egress data path runs from the workload through Network Firewall and the NAT and internet gateways to the test endpoint. The alarm pipeline runs from CloudWatch through Amazon Simple Notification Service (Amazon SNS) and the webhook AWS Lambda function to DevOps Agent.
Figure 1: The sample workload
To use this with your own workload, you need a CloudWatch alarm that detects the connectivity problem and the webhook pipeline (SNS topic and Lambda function) that delivers it to DevOps Agent. The agent reads your firewall configuration, logs, and CloudTrail through AWS APIs, so no additional instrumentation is needed on the firewall side.
To follow along with this post, you need:
Login
npm install -g aws-cdkClone the project and deploy it into us-east-1 with one command (set awsRegion to use another AWS Region).
git clone https://github.com/aws-samples/sample-accelerating-aws-network-firewall-troubleshooting-with-aws-devops-agent.git
cd sample-accelerating-aws-network-firewall-troubleshooting-with-aws-devops-agent
bash scripts/deploy.shThe script checks prerequisites, installs dependencies, compiles and tests, and bootstraps the CDK if needed. It then deploys all the stacks from a clean baseline and prints the outputs, including the status-page URL and sign-in details.
https://<random-id>.cloudfront.net address).To connect AWS DevOps Agent to the alarm pipeline
nf-devops-agent-space Agent Space created by the CDK deployment.nf-devops-agent-webhook-credentials AWS Secrets Manager secret, so there is no AWS CLI or console step. Until you set it, the bridge Lambda function sees a placeholder and skips delivery.nf-devops-agent-webhook and use the Test tab with this event.
{
"Records": [
{
"Sns": {
"Message": "{\"AlarmName\":\"TEST-webhook-verification\",\"AlarmDescription\":\"[TEST] Webhook integration test - not a real alarm.\",\"NewStateValue\":\"ALARM\",\"NewStateReason\":\"[TEST] Manual webhook connectivity test. Safe to ignore.\",\"Region\":\"us-east-1\"}"
}
}
]
}200 response confirms the path, and a test investigation appears in the DevOps Agent Operator Web App view.Every scenario reaches DevOps Agent the same way. A CloudWatch alarm moves to ALARM and notifies the SNS topic. Amazon SNS invokes a Lambda function. The function reads the webhook URL and signing secret from Secrets Manager, signs an alarm payload, and POSTs it to the DevOps Agent webhook (as shown in Figure 1). Amazon SNS also provides delivery retries, fan-out to other subscribers, and cross-account publishing.
Alarm-1 watches the DroppedPackets metric, summed across the stateful streams, and triggers when drops rise above a baseline threshold. This requires no workload or custom metric and works on an already-deployed firewall. However, it only tells you that the firewall is dropping packets, not which rule is responsible.Alarm-2 and Alarm-3 watch a custom metric from a connectivity check. Use this for an alarm tied to user-facing impact or to tell one traffic path from another, which requires running a component that emits the metric.| Alarm | Source | Triggers when |
| Alarm-1 | Native AWS/NetworkFirewall DroppedPackets |
The firewall’s dropped-packet count rises above the baseline |
| Alarm-2 | Custom application health metric | The port 9142 (TCP) connectivity check to the test endpoint is being dropped |
| Alarm-3 | Custom application health metric | The cross Availability Zone connectivity check is being dropped |
Work through each of the scenarios one at a time, following the same cycle. Interrupt network connectivity, watch the alarm trigger, let DevOps Agent investigate, apply the recommended fix, and confirm recovery before moving on.
The status-page cards follow the live CloudWatch alarm state. A card shows a green dot and the word Healthy when its alarm is clear, and a red dot and the word DROPPED when its alarm triggers. In the DROPPED state the card also adds a Condition: line describing what’s being dropped, which isn’t shown when the card is healthy. Network Firewall applies changes to new flows, so a change shows within a minute or two. Recovery comes from the mitigation DevOps Agent recommends, which you review and apply.
At baseline, the rg-domain Suricata domain rule group denies only an unused placeholder, so the test endpoint stays reachable. The rule group inspects the TLS Server Name Indication (SNI) on each outbound connection and drops any that matches a denied domain. The exact rule syntax and console steps follow.
To add the domain deny rule
rg-domain rule group to open its details page.blocked.placeholder.invalid, so nothing real is denied). Leave those in place. Find the <app-endpoint-dns> value for Scenario 1 in the deployment script output (a Nework Load Balancer (NLB) DNS name such as NfTest-AppNl-a1b2C3dEf4G5-1234abcd5678efgh.elb.us-east-1.amazonaws.com). On a new line below the existing rules, add a drop rule that matches that DNS name on the TLS SNI, then choose Save.
drop tls $HOME_NET any -> $EXTERNAL_NET any (ssl_state:client_hello; tls.sni; content:"<app-endpoint-dns>"; startswith; nocase; endswith; msg:"S1 domain denylist"; flow:to_server, established; sid:2000002; rev:1;)sid 2000002).
Figure 2: Scenario 1 – Firewall rule change blocking the connection
What happens. The workload’s HTTPS check to the test endpoint times out, the “AWS/NetworkFirewall DroppedPackets metric climbs above baseline, and Alarm-1 moves to ALARM. The Scenario 1 card reads DROPPED (with the condition Firewall dropping the monitored domain on its allow/deny rules), while the Scenario 2 and Scenario 3 cards stay Healthy (Figure 3). On the topology, the alarm pipeline from CloudWatch through Amazon SNS and Lambda to DevOps Agent and the workload-to-firewall inspect lines both turn amber, which the legend defines as collateral / alarm active, because the packets are now dropped at the firewall. To demonstrate the resulting failure, the HTTPS · SNI line from the internet gateway to the test endpoint is shown in red, which the legend defines as dropped (root cause).
Figure 3: Scenario 1 active – Traffic blocked at the firewall
Let DevOps Agent investigate. The agent runs several lines of investigation in parallel and correlates them:
DroppedPackets metric and correlates the spike with a simultaneous drop in passed packets, confirming the firewall is actively blocking traffic.ALERT log and finds the workload’s TLS connections to the test endpoint blocked by the S1 domain denylist rule.UpdateRuleGroup call that added the deny rule, identifying the user, role, and timestamp approximately one minute before the drops began.FirewallPolicyChangeProtection to prevent unauthorized changes.In the DevOps Agent Operator Web App view, the agent first restates the Alarm-1 trigger and confirms the firewall is dropping packets above the threshold (Figure 4).
Figure 4: Scenario 1 – The symptom
Next, the agent identifies the root cause: a manual update to the rg-domain rule group that added a domain deny rule (SID 2000002) shortly before the alarm fired, blocking TLS connections to the ELB endpoint (Figure 5).
Figure 5: Scenario 1 – The root cause
Finally, the agent presents a mitigation plan, recommending you remove the problematic deny rule (SID 2000002) to restore connectivity (Figure 6).
Figure 6: Scenario 1 – The mitigation plan
Note: In a real-world environment, this type of rule typically exists for a reason. Before removing it, verify whether it was intentional but scoped too broadly. If so, refine the rule to block only unauthorized endpoints rather than removing it entirely.
Confirm recovery. Apply the change the agent recommends. After the deny entry is gone, DroppedPackets falls back to baseline, Alarm-1 clears, and the card returns to green. Move on to Scenario 2.
At baseline, the rg-stateless-priority stateless rule group keeps the allow rule at priority 100 and the drop rule at 200 for the test class, TCP destination port 9142. The workload opens a TCP connection to the test endpoint on this port. Lower priority numbers evaluate first, so the allow rule wins. This scenario uses port 9142 instead of 443 to demonstrate a stateless rule, which matches on the packet’s 5-tuple (protocol, ports, addresses) rather than application content.
Introduce the change. Invert the two rule priorities so the drop rule evaluates before the allow rule. This is the kind of change a rushed rule edit can introduce.
To invert the stateless rule priorities
rg-stateless-priority rule group to open its details page.100 to 300 (any number higher than the drop rule’s 200 works). You only need to move one rule, and using 300 avoids a clash with the drop rule that already sits at 200. Network Firewall evaluates the lowest priority number first, so the (Action: Drop) rule at 200 now wins for this traffic class, ahead of the (Action: Pass) rule at 300.
Figure 7: Scenario 2 – Rule priority change blocking the traffic class
What happens. The drop rule now wins, the TCP connection to the test endpoint on port 9142 times out, the StatelessRuleFailures metric climbs above baseline, and Alarm-2 moves to ALARM. The Scenario 2 card reads DROPPED (with the condition Stateless rules dropping the monitored traffic class), while the Scenario 1 and Scenario 3 cards stay Healthy (Figure 8). On the topology, the alarm pipeline from CloudWatch through Amazon SNS and Lambda to DevOps Agent and the workload-to-firewall inspect lines both turn amber, which the legend defines as collateral / alarm active, because the packets are now dropped at the firewall. To demonstrate the resulting failure, the TLS :9142 line from the internet gateway to the test endpoint is shown in red, which the legend defines as dropped (root cause).
Figure 8: Scenario 2 active
Let DevOps Agent investigate. A stateless drop happens before traffic reaches the stateful inspection engine, so it produces no ALERT log entries. The agent turns to configuration and flow logs instead:
UpdateRuleGroup call that inverted the priorities, identifying the user, role, and timestamp about a minute before the alarm.In the DevOps Agent Operator Web App view, the agent first restates the Alarm-2 trigger and confirms that a workload connectivity health check is failing because the firewall’s stateless rules are dropping egress (Figure 9).
Figure 9: Scenario 2 – The symptom
Next, the agent identifies the root cause, using the rule-group state and CloudTrail to pinpoint the conflicting DROP/PASS rules, where the new DROP rule’s lower priority number makes it match first (Figure 10).
Figure 10: Scenario 2 – The root cause
Finally, the agent presents a mitigation plan, recommending you remove the conflicting DROP rule at priority 200 to restore traffic flow (Figure 11).
Figure 11: Scenario 2 – The mitigation plan
Confirm recovery. Apply the change the agent recommends. After the allow rule is ahead of the drop rule again, Alarm-2 clears and the card returns to green. Move on to Scenario 3.
At baseline, the protected subnet in each Availability Zone routes its egress through the firewall endpoint in that same Availability Zone , and the matching return route uses that same endpoint. One endpoint sees both directions of the flow, so the stateful engine completes the handshake. The workload runs in the protected subnet in us-east-1a (CIDR 10.0.4.0/24), so at baseline its egress and its return both use the us-east-1a firewall endpoint.
Introduce the change. Make the flow asymmetric by sending egress out one Availability Zone endpoint while the return comes back through the other. This takes two route edits, and both are required. With only the first edit the flow can still complete, so the alarm will not trigger until both are saved. It makes no firewall-policy change, mirroring a real multi-Availability-Zone routing mistake.
To create asymmetric cross Availability Zone routing
NfNetworkStack/SampleVpc/protectedSubnet1 route table (the us-east-1a protected subnet, where the workload runs). On the Routes tab, choose Edit routes. Its 0.0.0.0/0 route currently targets the us-east-1a firewall endpoint. For the target, choose Gateway Load Balancer Endpoint and select the us-east-1b firewall endpoint, then choose Save changes.NfNetworkStack/SampleVpc/publicSubnet2 route table (the us-east-1b public subnet, where egress now exits). Choose Edit routes, then Add route. For the destination enter the workload CIDR 10.0.4.0/24. For the target, choose Gateway Load Balancer Endpoint and select the us-east-1a firewall endpoint. Choose Save changes.After both edits, a flow’s egress leaves through the us-east-1b endpoint while its return is directed to the us-east-1a endpoint. Neither endpoint sees the whole flow.
Figure 12: Scenario 3 routing change breaking the flow’s symmetry
What happens. A new connection leaves through one endpoint. Its return arrives at the other endpoint, which never saw the connection open, so the handshake fails. Unlike Scenarios 1 and 2, this affects the whole subnet, so all egress stops and Alarm-2 and Alarm-3 both move to ALARM. The AWS/NetworkFirewall DroppedPackets alarm (Alarm-1) stays quiet because no endpoint is making a drop decision. The flow is lost to asymmetric routing rather than counted as a firewall drop. This is why monitoring application connectivity matters. A routing fault is invisible to the firewall’s own drop counter. On the status page, the Scenario 2 card reads DROPPED (with the condition “Stateless rules dropping the monitored traffic class”) and the Scenario 3 card reads DROPPED (with the condition Return traffic dropped by asymmetric cross-Availability-Zone routing), while the Scenario 1 card stays Healthy (Figure 13). On the topology, the alarm pipeline from CloudWatch through Amazon SNS and Lambda to DevOps Agent and the workload-to-firewall inspect lines both turn amber, which the legend defines as collateral / alarm active, while the egress path from the firewall through the NAT gateway and the TLS :9142 and HTTPS · routing lines to the test endpoint turn red, which the legend defines as dropped (root cause).
Figure 13: Scenario 3 – The status page during a path-wide outage
Let DevOps Agent investigate. Both Alarm-2 and Alarm-3 fire in the same datapoint. DevOps Agent recognizes them as linked and merges them into a single investigation:
DescribeRouteTables and finds the egress route pointing at one Availability Zone firewall endpoint while the return route points at the other.ReplaceRoute and CreateRoute calls by the same user, about a minute before both alarms fired.A mitigation plan is a recommendation you review, not an automatic change, and the right fix depends on the intended design. Restoring symmetric routing can mean sending the workload subnet’s egress back through its own-Availability-Zone firewall endpoint (this sample’s architecture) or, in a design that doesn’t inspect this path, back through a NAT gateway. The agent infers a plausible target from what it can observe, so review the specific route it proposes against your intended topology before you apply it. (Connecting your pipeline or infrastructure-as-code, covered in the next section, lets the agent recommend the target that matches your design.)
In the DevOps Agent Operator Web App view, the agent restates the Alarm-3 (AsymmetricFlowFailures) trigger and confirms the workload’s egress to a monitored endpoint is being blocked by the Network Firewall (Figure 14).
Figure 14: Scenario 3 – The symptom
Next, the agent identifies the root cause: manual route table changes that created cross-AZ asymmetric routing through the network firewall, breaking its symmetric routing requirement (Figure 15)
Figure 15: Scenario 3 – The root cause
Finally, the agent presents a mitigation plan, recommending you restore symmetric routing by pointing protectedSubnet1‘s default route back to the same Availability Zone firewall endpoint, so one endpoint sees both directions of the flow again (Figure 16).
Figure 16: Scenario 3 – The mitigation plan
Confirm recovery. Apply the change the agent recommends, after checking the route target matches your intended design. After the workload subnet’s egress and return use the same Availability Zone firewall endpoint again, the control probe recovers, the alarms clear, and every card returns to green.
In production a single change can trigger several alarms at the same time, as Scenario 3 shows. DevOps Agent links related investigations and works them as one, so you review a single root cause. You can validate the linked findings or unlink an alarm to investigate it independently. If you would rather collapse alarms before they reach the agent, you can add correlation logic in the bridge Lambda function, buffering and grouping by firewall. You can also add email, Amazon Simple Queue Service (Amazon SQS), or HTTP subscribers to the SNS topic, or add the webhook Lambda function to a topic you already run. DevOps Agent produces a mitigation plan but does not change your environment on its own.
You can also give the agent more to work with. DevOps Agent connects to source repositories and CI/CD pipelines, integrating with GitHub (including GitHub Enterprise Server and GitLab Self-Managed through a private connection). It can associate AWS resources with deployments of AWS CloudFormation, AWS CDK, Amazon Elastic Container Registry (Amazon ECR) images, and Terraform. With deployed configuration and recent deployment events in view, the agent correlates the disruption against the change that introduced it and recommends a fix matching your intended design. For this sample, that means recommending the workload subnet’s own Availability Zone firewall endpoint rather than a generic symmetric path.
DevOps Agent also supports proactive incident prevention. It analyzes patterns across past investigations and delivers recommendations to prevent similar issues from recurring, including governance recommendations that strengthen deployment processes and pipeline controls. For Network Firewall rule changes, this means the agent can recommend guardrails for your CI/CD pipeline based on the classes of misconfigurations it has already resolved. You can access these recommendations through the Improvements page in the DevOps Agent Operator Web App.
Clean up the environment with one command.
bash scripts/destroy.shIt reverts any active scenario, runs cdk destroy for all stacks, and sweeps for stragglers by the Project = nf-devops-agent tag. The main cost drivers are the two Network Firewall endpoints, the NAT gateways (one in the main VPC for each Availability Zone, one in the test-endpoint VPC), and the test endpoint’s load balancers. Each of these bills at an hourly rate for as long as it’s provisioned, whether or not traffic is flowing, so a stack left running continues to accrue charges around the clock even while idle. Running the scenarios and tearing the stack down the same day limits the cost to a few active hours rather than days of idle hourly charges.
In this post, we showed you how AWS DevOps Agent accelerates troubleshooting for three common network firewall connectivity issues. The first was a domain deny list. The second was a stateless priority inversion. The third was an asymmetric cross-AZ routing drop. For each one, DevOps Agent investigated the drop and returned a root cause with a mitigation plan you approve before applying. The first scenario triggered on a prebuilt Network Firewall metric, and the other two on application health metrics. That shows both ways to alarm on a firewall problem through one pipeline.
The pattern isn’t specific to Network Firewall. The same flow fits any service that emits CloudWatch metrics and logs, such as AWS WAF, security groups, and network ACLs. Clone the sample repository to explore the solution, then apply what you learn to your own firewall, application, and alarms. For more details, see the AWS Network Firewall Developer Guide and the AWS Network Firewall pricing page. Start with the Getting Started with AWS DevOps Agent guide to connect your first webhook.
Security Operations Centers (SOCs) serve as a critical line of defense against today's constantly evolving cybersecurity threats. At the heart of these teams are SOC analysts, who monitor, detect, and respond around the clock to potential attacks.
The post The Life of a SOC Analyst: Responsibilities, Challenges, and Strategies for Success appeared first on Black Hills Information Security, Inc..
The conversation around AI-powered threats has focused heavily on the attacker side. What models can do, what vulnerabilities they can find, how fast they can chain exploits together. But the more important question for security practitioners is simpler and harder. Is your detection posture ready for what’s already happening?
AI has lowered the barrier to entry for sophisticated attacks. Threat actors don’t need to be experts to leverage LLMs for obfuscation, exploit development, or chaining attack steps that previously required deep manual skill. The speed and volume of attacks is increasing as a result. Detection engineering is part of how defenders respond. And most organizations are further behind than they realize, especially because many detection engineering programs live in their own siloed “ivory tower”, detached from the realities of what SOC analysts are handling.
At Intezer, AI-powered detection engineering runs as part of a closed loop with automated, forensic triage and investigation. Every alert investigated across the environment feeds new signal back into detection logic, so coverage improves continuously instead of drifting between quarterly tuning cycles. Detection working in concert with triage and investigation is what a fully optimized security environment looks like against AI-powered attackers.
In this article, we’ll focus on how to improve detection engineering practices in general.
The fundamental problem is that most detection programs were built for a world where attack volume was bounded by attacker expertise. That constraint is being removed. LLM-augmented attacks can move faster, produce more permutations, and adapt more readily to your environment than traditional campaigns. A detection posture built on static indicators and periodic tuning cycles can’t keep up.
There are three places this shows up in practice.
First, most organizations are only covering technique-level MITRE ATT&CK mappings, not sub-techniques. When you claim T1059 is covered because you have a rule for T1059.001, you’re exposing yourself to real risk hiding beneath that coverage number. Sub-techniques carry distinct behaviors that may exist in your environment and go completely undetected. High-level coverage scores look good in reports and obscure what’s actually happening. The risk lives at the sub-technique level.
Second, IOC-based detections are becoming a liability at scale. IP addresses, file hashes, domains are valuable in incident response but brittle as primary detection logic. Their half-life is short, adversaries burn them readily, and maintaining a large list of active IOCs creates noise without proportionate signal. Organizations that lead with IOCs end up toggling rules on and off constantly, adding friction without improving coverage. The maintenance cost compounds without a meaningful security return.
Third, telemetry that looks healthy often isn’t. A Windows Event ID 4688 without command-line logging enabled is an example. You’re paying to ingest it, it shows up in your coverage maps, but it provides no actionable data when something fires. Unmapped or broken telemetry creates the appearance of coverage where none actually exists. Before you write a new rule, validate that the data it depends on actually contains what you think it does.
Behavioral detections are built around what attackers do across a campaign, not what artifacts they happened to leave behind in a specific incident. Techniques, sequences, tool patterns, execution chains persist across campaigns, across threat actors, and even across malware families. A behavioral detection written well today has a much longer useful life than any IOC-based rule.
The shift to behavioral detection isn’t just a philosophy, it requires specific changes to how rules are built and maintained.
Score-based detection logic is one of the most underused approaches in enterprise SIEMs. If you’re running Splunk, Sumo Logic, or Cortex XDR, score-based rules let you assign weighted values to individual signals and alert when combinations cross a threshold. Individual signals that are weak in isolation, a process executing from an unusual path, a network connection to an uncommon destination, a scheduled task created outside business hours, become meaningful together. Noise goes down. True positive rate goes up. And the system stays tunable as your environment changes.
Permutation testing is the other discipline most detection programs skip. LLMs make it straightforward to generate attack variants at volume. Defenders should be doing the same before releasing rules. If a detection rule only catches one specific implementation of a technique, an attacker using a slightly different toolchain or execution order will evade it. Testing rules against a range of permutations before production deployment closes gaps that post-deployment tuning will miss.
The traditional cycle, write a rule, deploy it, wait for something to fire, tune reactively when it generates too many false positives, is too slow for the current threat environment. By the time you’ve finished tuning a rule for last quarter’s threat, new attack patterns are already in the wild.
The cycle needs to compress at every stage. Prototype rules should be tested in isolated environments before they reach production. Sandboxes and virtual machines can be spun up quickly in the same pipeline as rule development, giving you a controlled validation environment. Rules that are tuned before deployment don’t flood the SOC on their first day, and analysts who aren’t buried in false positives from new rules are analysts who can actually investigate real threats.
Continuous monitoring closes the loop. Every alert that fires, every verdict and every outcome feeds information back into the detection posture. Which rules are generating signal? Which ones are generating noise? Where are the coverage gaps that no existing rule addresses? Without this feedback loop, detection engineering becomes a periodic exercise rather than a continuously improving system.
A well integrated feedback loop investigates every single alert a detection creates, resolves false positives from critically important detections while continuously tuning the behavioral model to secure your organization and security detection pipeline.
Coverage benchmarks help set realistic expectations and give teams a concrete target. Based on what we see across enterprise environments:
Anything above 70% is usually inflated. Scores at this level typically reflect mapping sprawl across multiple MITRE versions, technique-level claims that obscure sub-technique gaps, or rules that are mapped but broken. Validate the underlying data before trusting the number.
The goal isn’t 100% coverage. That number isn’t achievable or meaningful. The goal is systematic, maintainable coverage of the techniques most relevant to your environment and your crown jewels, with the sub-technique depth to catch how those techniques are actually executed.
Mythos focused attention on a specific capability and that is autonomous chaining of exploit steps that previously required human guidance at each stage. A skilled researcher can still walk an LLM through finding a vulnerability, reaching exploit code, and overtaking an instruction pointer, but that process requires human direction at each transition. What makes autonomous chaining meaningful is that it removes the human from the loop on the attacker side.
The detection engineering response isn’t a new category of rule. It’s the same disciplines applied with more rigor and at higher speed. Attackers using LLMs are still executing against endpoints, still writing to disk or running in memory, still making network connections, still creating processes. The behaviors are recognizable. What changes is the volume of variants and the speed at which new campaigns emerge.
Score-based logic handles volume well because it doesn’t require a rule per variant. Permutation testing handles new variants better than reactive tuning because gaps are found before deployment rather than after. Behavioral coverage handles campaign evolution better than IOC maintenance because the underlying techniques persist even as the tooling changes.
This is where an integrated model matters most. AI-powered detection engineering delivers the most value when it doesn’t operate in isolation, and at Intezer it runs on the same loop as automated triage and investigation. The platform investigates 100% of alerts across endpoint, identity, cloud, network, and SIEM, and every verdict feeds directly back into detection, surfacing noisy rules, broken telemetry, and coverage gaps as they happen rather than at the next review. Detection, triage, and investigation reinforcing one another is what produces a fully optimized security environment, one that keeps pace as attacker AI accelerates.
The organizations that fare best against AI-powered threats will be the ones that already had a functioning detection engineering program, one built on behavioral logic, continuous feedback, and validated telemetry, before the threat landscape changed. Catching up under pressure is possible, but it’s harder and slower than building the discipline now.
The attacker’s AI is getting faster. The detection engineering cycle should be too.
Learn more about Intezer’s AI-powered detection engineering.
The post Detection engineering in the AI era appeared first on Intezer.
Email remains the primary entry point for phishing, fraud, malware, and Business Email Compromise (BEC) attacks. While Microsoft 365 provides a strong foundation, organizations need additional layers of protection to defend against increasingly sophisticated threats
Intezer already investigates 100% of your alerts and escalates fewer than 2% of them for human review. That part is handled.
The work does not stop there, though. Every SOC has its own routines wrapped around the investigation itself. The incident reports written in a particular format, the closure notes, the shift handoffs, the rules that decide who picks up which case. When we looked at how teams actually use AI Chat, our in-product investigation agent, more than a third of those conversations turned out to be the same repetitive tasks asked again and again. The same summaries. The same reports. The same closure notes.
Intezer’s AI SOC already runs agents around the clock to triage, investigate, and respond to your alerts on their own. Custom Agents is the next step. Now you can shape how that AI SOC works for your team. Add your own agents and automations on top of the ones Intezer runs out of the box, take more of the manual work off your analysts, and tailor the whole thing to the way your team actually operates.
Intezer ships with a set of agents and automations that handle triage, investigation, and response from day one. Custom Agents lets you build your own on top of them.
An agent is made up of three components:
You describe what you want done in plain language, choose when it should run, and pick what it can touch. It then runs on its own inside your Intezer environment, on the same engine that powers our investigation Agent (Chat).
1. Tell it what to do, in plain language. Write the instructions the way you would brief a new analyst. “Every morning, review the open case queue, close the clear false positives per our playbook, and leave a handoff note on the rest.” That is an agent.
2. Choose when it runs. Three trigger types cover most workflows:
3. Give it the right tools. Agents work across your whole stack which includes Intezer’s built-in toolset plus the SIEM, EDR, and identity tools you have already connected, including CrowdStrike, SentinelOne, Splunk, Microsoft Sentinel, and Entra ID. They do more than summarize. They take action by updating, commenting on, closing cases, and emailing a finished report to your team.
Take an Incident Report Writer agent illustrated above. We deliberately never shipped a single “Generate report” button, because no two teams want the same report. One team wants an executive summary up top, another wants the full timeline, another has a compliance format it has to match. So instead of a button, you put your format into the agent’s instructions, and it writes every report that way, every time.
The agent triggers on every escalated case an analyst has confirmed as a real threat. It reads the case, writes the report in your format, and emails it to your team’s inbox. The analyst makes the call. The paperwork writes itself.
That’s one agent. The point of Custom Agents is that you decide what they are.
Security teams do not trust black boxes, and they are right not to. Custom Agents is built so you can see and control everything an agent does.
This is how everything at Intezer works. AI executes, humans supervise. Custom Agents lets you decide what it executes.
We opened Custom Agents to a small group of alpha customers, and the best part has been watching what they build. Alongside the Incident Report Writer above, a few of the agents already running in production:
We are on a mission to build the AI SOC the industry has been promised but never delivered. One that does the work and earns the trust to do it. It runs autonomously, around the clock. It works alongside the people who supervise it, not over their heads. And it is never a black box. You can always open it up, question what it did, and change how it behaves.
Custom Agents is central to that vision. Triage, investigation, and response come built in. Everything particular to how your team operates, you build yourself. Because the strongest security teams have always run on their own playbooks, their own logic, and their own standards, and an AI SOC should be no different. It should not ship the same to everyone. No two SOCs are the same, and no two should be.
That is the point of Custom Agents. You decide what they are.
Custom Agents is available now in beta to Intezer customers, and it is free during the beta period. This is the moment to build, test, and tell us what you want it to do next.
See what your SOC could hand off. Book a demo.
If you are already an Intezer customer, you will find it under Custom Agents in the top menu.
The post Introducing Custom Agents: Automate your SOC, your way appeared first on Intezer.
The email landscape has fundamentally changed — and in 2026, there is no ambiguity left:
DMARC is no longer optional. It is enforced.
Over the past two years, the world’s largest mailbox providers — Google, Yahoo, and Microsoft — have implemented strict authentication requirements. Today, any domain sending bulk email must have properly configured SPF, DKIM, and DMARC, or risk outright rejection. [mailcop.net], [redsift.com]
This shift marks the end of the “open email era” and the beginning of a trust-based ecosystem where authentication is mandatory.
Unit 42's analysis of ClawHub revealed evasive malicious skills bypassing automated scanners to deploy infostealers and execute agentic financial fraud.
The post OpenClaw’s Skill Marketplace and the Emerging AI Supply Chain Threat appeared first on Unit 42.
We're all petrified about missing a critical event or misclassifying an alert, but when we're talking about incident response (IR), there are often hundreds if not thousands of alerts to parse through. It's easy to get caught up with one alert because it feels "too hot" or maybe not spend enough time looking into something that initially seems "too cold."
The post Finding the “Goldilocks” Zone: A Practical Approach to Alert Triage appeared first on Black Hills Information Security, Inc..
Attackers can move from access to exfiltration in 72 minutes. Learn how modern SOC teams close the speed gap with Unit 42's AI-driven automation, threat hunting, MDR and Managed XSIAM.
The post Inside the Modern SOC: The 72-Minute Race appeared first on Unit 42.
Amazon Web Services (AWS) is pleased to announce that the Spring 2026 System and Organization Controls (SOC) 1, 2, and 3 reports are now available. The reports cover 188 services over the 12-month period from April 1, 2025–March 31, 2026, giving customers a full year of assurance. These reports demonstrate our continuous commitment to adhering to the heightened expectations of cloud service providers.
Customers can download the Spring 2026 SOC 1 and 2 reports through AWS Artifact, a self-service portal for on-demand access to AWS compliance reports. Sign in to AWS Artifact in the AWS Management Console, or learn more at Getting Started with AWS Artifact. The SOC 3 report can be found on the AWS SOC Compliance Page and AWS Artifact.
AWS strives to continuously bring services into the scope of its compliance programs to help customers meet their architectural and regulatory needs. You can view the current list of services in scope on our Services in Scope page. As an AWS customer, you can reach out to your AWS account team if you have any questions or feedback about SOC compliance.
To learn more about AWS compliance and security programs, see AWS Compliance Programs.
If you have feedback about this post, submit comments in the Comments section below.
On May 22, 2026, Dutch financial-crime investigators walked into data centers in Dronten and Schiphol-Rijk and seized approximately 800 servers. The target was WorkTitans B.V., a hosting provider that, on the surface, looked like any other internet infrastructure company. What investigators uncovered, however, was something far more significant: a ghost operation built on sanctioned infrastructure, quietly serving as the backbone for some of Iran’s most active cyber espionage campaigns. The story starts a year earlier. In May 2025, the European Union sanctioned Stark Industries, an internet service provider linked to Russian information-warfare operations. Rather than shutting down, the people behind […]
The post The Server Seizure That Affects Also Iran’s Cyber Operations appeared first on Check Point Blog.
AWS Network Firewall now supports native attachment to AWS Transit Gateway. Customers commonly use Transit Gateway to route traffic from Amazon Virtual Private Cloud (Amazon VPC) networks to a centralized inspection VPC (a VPC dedicated to hosting firewall endpoints for traffic inspection) where their network firewall endpoints are deployed. This centralized deployment model reduces the need to have Network Firewall endpoints in each VPC, optimizing costs and providing a centralized point of network security control.
Customers deploying Network Firewall in a centralized deployment model using Transit Gateway have traditionally set up a dedicated inspection VPC with firewall subnets and managed the associated routing to direct traffic through the firewall. With native attachment, Network Firewall attaches directly to Transit Gateway, eliminating the need for the inspection VPC and enabling capabilities such as flexible cost allocation through Transit Gateway metering policies.
In this post, we explain what a Transit Gateway-attached network firewall is, the technical capabilities it unlocks, reasons to migrate to it, and how to perform the migration. For detailed step-by-step guidance on how to perform the migration using Terraform, AWS CloudFormation, or manually in the AWS Management Console, see the accompanying migration guide repository.
A Transit Gateway-attached network firewall simplifies your network architecture by eliminating the need for a dedicated inspection VPC. Instead of creating an inspection VPC with firewall subnets and configuring the associated routing, you create your network firewall and specify which Transit Gateway instance you want to attach it to. AWS deploys the firewall endpoints into an AWS-managed VPC on your behalf. You don’t own or manage that VPC. From your perspective, the firewall appears as a Transit Gateway network function attachment that you route traffic to, similar to other Transit Gateway attachments.
You might want to migrate to a Transit Gateway-attached network firewall for the following reasons:
Before migrating to a Transit Gateway-attached network firewall, gather the following information and keep these key considerations in mind.
When you create your new Transit Gateway-attached network firewall, you will need:
There are some important considerations to address while planning for this change.
Leave your existing Network Firewall setup unchanged while setting up the new Transit Gateway-attached firewall. With this approach, you can minimize potential downtime and test the new configuration before migrating production traffic.
The migration process varies depending on your current architecture. The following sections walk through the two most common centralized Network Firewall architectures and the high-level migration process for each. For detailed step-by-step guidance on how to perform the migration using Terraform, CloudFormation, or manually in the console, see the migration guide repository.
In this architecture, shown in the following diagram, you have a dedicated inspection VPC with your network firewall endpoints, and a separate dedicated egress VPC with your NAT gateways.
Figure 1: Centralized egress traffic inspection with Network Firewall and Transit Gateway, with inspection and egress separated into two VPCs.
The high-level migration process for this architecture is:
Figure 2: Post-migration architecture for Architecture 1, with the inspection VPC eliminated and traffic flowing through the Transit Gateway-attached Network Firewall to a dedicated egress VPC.
For the complete walkthrough of how to perform this migration:
In this architecture, shown in the following diagram, you have a single VPC that contains both your network firewall endpoints and your NAT gateways.
Figure 3: Centralized egress traffic inspection with Network Firewall and Transit Gateway, with inspection and egress combined in one VPC.
The migration process for this architecture follows the same high-level steps as Architecture 1.
Figure 4: Post-migration architecture for Architecture 2, with the combined VPC eliminated and traffic flowing through the Transit Gateway-attached Network Firewall to a dedicated egress VPC.
For the complete walkthrough of how to perform this migration, see:
Both architectures deploy the same new resources and use the same phased cutover approach. The differences are in the starting Transit Gateway routing structure (Architecture 1 has three route tables across two VPCs, Architecture 2 has two route tables in one VPC) and what you clean up at the end (two old VPCs instead of one). Both architectures converge to the same end state. For a detailed comparison, see the migration guide repository.
Regardless of which architecture you’re migrating from, follow these best practices to minimize risk.
The migration guide repository includes starting architecture CloudFormation and Terraform templates for both architectures, so you can deploy the exact starting environment in a development or test account and run through the entire migration process before touching production.
Test before you migrate. Create your new Transit Gateway-attached firewall in parallel with your existing setup. Use a test VPC to validate the new configuration. Verify that logging is working correctly and that the firewall alert logs show layer 7 traffic details, which confirms there is no asymmetric routing. Test both allowed and blocked traffic scenarios before migrating production traffic.
Migrate in phases. Start with a single, non-critical workload VPC. Update only that VPC’s routes to use the new firewall attachment. Monitor and verify application behavior and performance with the application owner before proceeding. When planning your migration order, migrate spoke VPCs that have east-west traffic between each other at the same time. During the phased migration, spokes on different firewall paths will have their east-west traffic traverse two stateful firewalls. Because each stateful firewall independently tracks connection state, traffic that enters through one firewall and returns through another appears as untracked, causing the firewalls to drop or incorrectly handle the return traffic. When you’re confident in the new firewall deployment, you can update the default route in your existing spoke route table to point to the new firewall, which moves all remaining spokes that share that route table at once. Keep your old firewall configuration active until all traffic is migrated.
Prepare a rollback plan. Document your current route table configurations before making changes. Keep your existing firewall and inspection VPC active during migration. If issues arise, revert the route table changes to restore the previous configuration. Decommission old resources after you’ve verified applications are operating as expected.
An important consideration during migration is maintaining your existing NAT gateway Elastic IP addresses. Many organizations have these IPs allowlisted with external partners, third-party services, or in firewall rules. Changing these IPs would require coordination with multiple stakeholders and could disrupt business operations.
During migration, you need both your old and new deployments to operate simultaneously, so you can validate the new setup without impacting production traffic. This means creating temporary NAT gateways with temporary Elastic IPs in the new egress VPC.
After you’ve confirmed the new firewall deployment is stable and production traffic has been successfully migrated, you can restore your original Elastic IPs. The process differs depending on your architecture:
For the detailed step-by-step procedure, see the EIP preservation steps in the migration guide repository.
In this post, we explained what a Transit Gateway-attached network firewall is and how it differs from the traditional inspection VPC model, the reasons to migrate including reduced architectural complexity and flexible cost allocation, what to prepare before starting, and the high-level migration process for the two most common centralized inspection architectures. We also covered best practices for minimizing downtime, handling east-west traffic between spokes during phased migration, and preserving your existing NAT gateway Elastic IPs.
With a Transit Gateway-attached network firewall, AWS manages the firewall endpoints and the underlying VPC on your behalf, eliminating the inspection VPC from your architecture and enabling flexible cost allocation through Transit Gateway metering policies. The phased migration approach covered in this post lets you run both firewalls in parallel, validate the new path with a single spoke VPC, and cut over the rest of your traffic when you are ready.
For detailed step-by-step guidance using Terraform, CloudFormation, or the AWS Management Console for both architectures covered in this post, see the migration guide repository. The repository includes starting architecture templates so you can practice the full migration end-to-end in a test account before migrating your production environment.
If you have feedback about this post, submit comments in the Comments section below.
