Leveraging Amazon GuardDuty suppression rules to eliminate noise

Thomas Haggath — Sun, 10 May 2026 05:52:48 +0000

What is GuardDuty?

Amazon GuardDuty is a continuous threat detection service that monitors, analyses, and processes data sources and logs across your AWS environment. It uses threat intelligence feeds (such as lists of malicious IP addresses, domains, and file hashes) combined with machine learning models to identify suspicious and potentially malicious activity without requiring you to deploy or manage any additional security software.

When enabled, GuardDuty automatically begins ingesting foundational data sources including AWS CloudTrail management events, VPC Flow Logs, and DNS query logs. Beyond these defaults, GuardDuty offers dedicated protection plans that extend coverage to additional services:

EKS Protection: audits Kubernetes API server logs for your EKS clusters
RDS Protection: monitors login activity for Amazon Aurora databases
S3 Protection: analyses CloudTrail data events for S3 object-level activity
Malware Protection: scans EBS volumes or S3 objects for malicious files
Lambda Protection: inspects network activity from Lambda function invocations
Runtime Monitoring: captures OS-level, network, and file events from EC2, ECS, and EKS workloads

When a potential threat is identified, GuardDuty generates a finding: a detailed security alert containing information about the affected resource, the threat actor, and the severity of the activity.

Example GuardDuty Finding Types

Finding types follow the format ThreatPurpose:ResourceType/ThreatFamilyName. The resource type in the name tells you which AWS service was targeted. Below are representative examples across the main categories.

EC2

EC2 findings typically relate to network-level threats observed via VPC Flow Logs and DNS logs.

CryptoCurrency:EC2/BitcoinTool.B!DNS: an EC2 instance is querying a domain associated with cryptocurrency mining pools
Trojan:EC2/BlackholeTraffic: an instance is communicating with an IP address known to be a black-hole used by malware command-and-control infrastructure
UnauthorizedAccess:EC2/TorIPCaller: an EC2 instance is being accessed from a Tor exit node

IAM

IAM findings use GuardDuty's anomaly detection model to flag unusual API call patterns across CloudTrail management events.

UnauthorizedAccess:IAMUser/ConsoleLoginSuccess.B: a successful console login from an unusual geographic location
CredentialAccess:IAMUser/AnomalousBehavior: an IAM principal is making API calls in a pattern inconsistent with its historical baseline
Persistence:IAMUser/UserPermissions: an IAM entity is modifying policies or creating new users in a manner consistent with persistence techniques

S3

S3 findings surface data-access threats against your buckets.

Policy:S3/BucketPublicAccessGranted: an IAM principal has disabled block-public-access settings on a bucket
Exfiltration:S3/MaliciousIPCaller: S3 API calls are being made from a known malicious IP address
Discovery:S3/MaliciousIPCaller.Custom: a source IP on your custom threat list is enumerating S3 buckets

EKS

EKS Protection findings come from Kubernetes audit log analysis.

CredentialAccess:Kubernetes/MaliciousIPCaller: a Kubernetes API call to retrieve secrets was made from a known malicious IP
Execution:Kubernetes/ExecInKubernetes: a command was executed inside a running container via kubectl exec
PrivilegeEscalation:Kubernetes/PrivilegedContainer: a privileged container was launched, which could allow a breakout to the underlying host

RDS

RDS Protection findings are generated from login activity to Amazon Aurora clusters.

CredentialAccess:RDS/AnomalousBehavior.SuccessfulLogin: a successful database login from an unusual user or location
CredentialAccess:RDS/MaliciousIPCaller.FailedLogin: failed login attempts originating from a known malicious IP address

Attack Sequences (Extended Threat Detection)

GuardDuty's Extended Threat Detection correlates findings across multiple services and time windows to surface multi-stage attacks as a single high-severity finding.

AttackSequence:IAM/CompromisedCredentials: a sequence of IAM events indicating credential compromise and subsequent lateral movement
AttackSequence:S3/CompromisedData: a chain of events suggesting S3 data was discovered and then exfiltrated
AttackSequence:EKS/CompromisedCluster: correlated signals pointing to a compromised Kubernetes cluster

What are Suppression Rules?

A suppression rule is a filter you define in GuardDuty that automatically archives any new finding that matches its criteria. Suppressed findings are never deleted. GuardDuty still generates them and stores them for 90 days, but they are immediately moved to the archived state and do not appear in your active findings queue.

How they work

You define a suppression rule using one or more filter attributes (finding type, severity, resource tags, EC2 instance ID, S3 bucket name, etc.) combined with match operators:

Operator	Behaviour
`Equals` / `NotEquals`	Exact match or exclusion
`Matches` / `NotMatches`	Wildcard pattern match
`GreaterThan` / `LessThan`	Numeric comparison (e.g. severity score)

Rules can be as broad as suppressing an entire finding type (e.g. all CryptoCurrency:EC2/* findings) or as granular as suppressing a specific finding type only when it fires against a resource tagged Environment: dev.

Why use them?

In a mature AWS environment, certain findings will reliably represent known-good activity: a penetration testing EC2 instance that legitimately communicates over unusual ports, a NAT gateway that generates high-volume DNS findings, or a deployment pipeline that makes bulk S3 API calls. Without suppression rules, these recurring false positives dilute your signal-to-noise ratio and increase alert fatigue.

Suppressed findings are also excluded from downstream integrations and are not forwarded to AWS Security Hub, Amazon EventBridge, Amazon Detective, or Amazon S3 exports. This means your SIEM, ticketing system, or on-call paging tool stays quiet for findings you have already triaged and accepted.

Important caveat

GuardDuty's Extended Threat Detection relies on individual findings as signals when building attack sequences. Broadly suppressing finding types can prevent attack sequence findings from being generated, because the archived signals are excluded from correlation. Suppress at the most specific level you can; prefer resource-scoped rules over type-wide ones.

GuardDuty IP Sets

GuardDuty lets you upload custom lists of IP addresses (and domains) to fine-tune its detection behaviour. These lists come in two flavours: Trusted IP lists and Threat Intel lists (also called threat lists), described below.

Both are stored as plain-text files in S3 (one entry per line, supporting CIDR notation for IP ranges) and activated per GuardDuty detector. GuardDuty now recommends using entity lists, which can contain IP addresses, domain names, or both in the same list, over the legacy IP-only format.

Trusted IP Lists

A trusted IP list contains IP addresses or CIDR ranges that you consider safe sources of traffic, such as your corporate VPN egress IPs, an office network, or a known third-party security scanner you have authorised. GuardDuty does not generate findings for activity originating from entries on a trusted IP list.

Trusted lists are useful when you have infrastructure that legitimately behaves in ways that would otherwise trigger findings, and you want to suppress an entire source rather than write individual suppression rules per finding type.

Threat Intel Lists

A threat intel list contains IP addresses or domains you have identified as known malicious sources, such as IP ranges from your own incident response investigations or feeds from a commercial threat intelligence provider. When GuardDuty observes activity involving an entry on a threat list, it generates a finding even if that IP would not otherwise have triggered one.

Threat lists let you operationalise your own threat intelligence and ensure GuardDuty alerts on adversary infrastructure that AWS's built-in feeds may not yet include.

Limits

Up to 6 trusted IP lists and 6 threat intel lists per GuardDuty detector per region
Maximum 200,000 entries per list (IP addresses or CIDRs)
Lists must be stored in S3 and the GuardDuty service role must have s3:GetObject permission on the bucket

The Difference Between Trusted Lists and Suppression Rules

Both trusted lists and suppression rules can silence GuardDuty findings for known-good activity, but they operate at different layers and have meaningfully different behaviours.

	Trusted IP / Entity Lists	Suppression Rules
Scope	Source IP address or domain	Any finding attribute (type, severity, resource, tags, account, etc.)
Mechanism	Prevents the finding from being generated	Generates the finding, then immediately archives it
Finding stored?	No (the finding is never created)	Yes - archived for 90 days, fully queryable
Attack sequence impact	No signal created, so no correlation possible	Archived signals are excluded from correlation
Downstream forwarding	N/A (nothing to forward)	Suppressed findings are not sent to Security Hub, EventBridge, or S3 exports
Granularity	IP/domain only	Fine-grained: combine multiple attributes with AND logic
Best used for	Trusted infrastructure sources you always want to ignore	Known false positives scoped by resource, tag, region, or finding subtype

When to use each

Use a trusted IP list when the noise originates from a specific, stable set of IP addresses you fully control or trust: your VPN, a penetration testing host, or an authorised scanner. It is the bluntest instrument but requires no maintenance as new finding types emerge.

Use a suppression rule when you need more precision — for example, suppressing CryptoCurrency:EC2/BitcoinTool.B only for instances tagged Purpose: mining-research, while keeping the finding active for all other EC2 instances. Suppression rules also give you the audit trail of archived findings, which trusted lists do not.

In practice, most teams use both: trusted lists for known-good source infrastructure, and suppression rules for finding-type-specific or resource-scoped noise reduction.

Example Finding: IAM Credential Use Outside AWS

The finding type UnauthorizedAccess:IAMUser/InstanceCredentialExfiltration.OutsideAWS fires when instance profile credentials (issued via the EC2 metadata service) are used from an IP address that does not belong to AWS infrastructure. This is a high-fidelity indicator of credential theft, since legitimate use of instance credentials should never originate outside of AWS.

{
  "schemaVersion": "2.0",
  "accountId": "123456789012",
  "region": "eu-west-1",
  "partition": "aws",
  "id": "a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4",
  "arn": "arn:aws:guardduty:eu-west-1:123456789012:detector/abc123def456/finding/a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4",
  "type": "UnauthorizedAccess:IAMUser/InstanceCredentialExfiltration.OutsideAWS",
  "title": "Instance credential used from external IP address not associated with AWS",
  "description": "Credentials created exclusively for EC2 instance i-0abc123def456789a (via role WebAppInstanceRole) were used from external IP address 185.220.101.47, which is not associated with AWS infrastructure.",
  "severity": 8.0,
  "createdAt": "2026-05-10T09:14:32Z",
  "updatedAt": "2026-05-10T09:14:32Z",
  "service": {
    "serviceName": "guardduty",
    "detectorId": "abc123def456",
    "action": {
      "actionType": "AWS_API_CALL",
      "awsApiCallAction": {
        "api": "GetSecretValue",
        "serviceName": "secretsmanager.amazonaws.com",
        "callerType": "Remote IP",
        "remoteIpDetails": {
          "ipAddressV4": "185.220.101.47",
          "organization": {
            "asn": "4244",
            "asnOrg": "Tor Project Exit Node",
            "isp": "Quintex Alliance Consulting",
            "org": "Quintex Alliance Consulting"
          },
          "country": { "countryName": "United States" },
          "city": { "cityName": "Atlanta" },
          "geoLocation": { "lat": 33.749, "lon": -84.388 }
        },
        "affectedResources": {}
      }
    },
    "evidence": {
      "threatIntelligenceDetails": [
        {
          "threatListName": "ProofPoint ET Intelligence",
          "threatNames": ["TorExitNode"]
        }
      ]
    },
    "archived": false,
    "count": 1
  },
  "resource": {
    "resourceType": "AccessKey",
    "accessKeyDetails": {
      "accessKeyId": "ASIAQRSTUVWXYZ123456",
      "principalId": "AROABC123DEF456GHI789:i-0abc123def456789a",
      "userType": "AssumedRole",
      "userName": "WebAppInstanceRole"
    }
  }
}

Key fields to note:

type: the finding identifier, useful as the primary filter attribute in a suppression rule
severity: scored 8.0 (High) on GuardDuty's 1-10 scale; credentials used outside AWS are rarely false positives
service.action.awsApiCallAction.api: the specific API call made with the stolen credentials (GetSecretValue here indicates the attacker was targeting secrets)
service.action.awsApiCallAction.remoteIpDetails: the external IP and enriched geo/ASN data GuardDuty adds automatically
service.evidence.threatIntelligenceDetails: shows the credential was used from a known Tor exit node, corroborating the alert
resource.accessKeyDetails.principalId: the AROA... prefix confirms this is an assumed-role session tied to an EC2 instance, not a long-term IAM user key
Summary
In this post we covered the core building blocks of GuardDuty and how to use them to manage signal quality in a real AWS environment.

We started with an overview of what GuardDuty is: a managed threat detection service that ingests foundational data sources (CloudTrail, VPC Flow Logs, DNS logs) and optional protection plans covering EKS, RDS, S3, Lambda, and runtime workloads, generating findings when suspicious activity is detected.

We then looked at the main finding type categories. Each finding follows the ThreatPurpose:ResourceType/ThreatFamilyName format and targets a specific AWS resource type, whether that is an EC2 instance, an IAM principal, an S3 bucket, a Kubernetes cluster, an RDS database, or a correlated attack sequence spanning multiple services.

From there we explored suppression rules: filters that automatically archive matching findings without deleting them. They keep your active queue clean while preserving a 90-day audit trail, but broad rules can interfere with Extended Threat Detection's ability to correlate attack sequences, so specificity matters.

We covered GuardDuty's IP sets and entity lists, explaining the distinction between trusted lists (which prevent findings from being generated for known-good sources) and threat intel lists (which force findings for known-bad sources you supply yourself).

We then compared trusted lists against suppression rules directly, highlighting that the key difference is not just scope but mechanism: trusted lists suppress at the source level before a finding exists, while suppression rules operate after generation, giving you the archived record.

Finally, we walked through a realistic example finding for UnauthorizedAccess:IAMUser/InstanceCredentialExfiltration.OutsideAWS to show what GuardDuty's JSON output looks like in practice and which fields matter most when triaging or writing suppression logic.

DEV Community: Thomas Haggath