Migrate Splunk detection rules to Microsoft Sentinel

Article
03/11/2024

This article describes how to identify, compare, and migrate your Splunk detection rules to Microsoft Sentinel built-in rules.

If you want to migrate your Splunk Observability deployment, learn more about how to migrate from Splunk to Azure Monitor Logs.

Identify and migrate rules

Microsoft Sentinel uses machine learning analytics to create high-fidelity and actionable incidents, and some of your existing detections may be redundant in Microsoft Sentinel. Therefore, don't migrate all of your detection and analytics rules blindly. Review these considerations as you identify your existing detection rules.

Make sure to select use cases that justify rule migration, considering business priority and efficiency.
Check that you understand Microsoft Sentinel rule types.
Check that you understand the rule terminology.
Review any rules that haven't triggered any alerts in the past 6-12 months, and determine whether they're still relevant.
Eliminate low-level threats or alerts that you routinely ignore.
Use existing functionality, and check whether Microsoft Sentinel's built-in analytics rules might address your current use cases. Because Microsoft Sentinel uses machine learning analytics to produce high-fidelity and actionable incidents, it's likely that some of your existing detections won't be required anymore.
Confirm connected data sources and review your data connection methods. Revisit data collection conversations to ensure data depth and breadth across the use cases you plan to detect.
Test the capabilities of the SIEM migration experience to determine if the automated translation is suitable.
Explore community resources such as the SOC Prime Threat Detection Marketplace to check whether your rules are available.
Consider whether an online query converter such as Uncoder.io might work for your rules.
If rules aren't available or can't be converted, they need to be created manually, using a KQL query. Review the rules mapping to create new queries.

Learn more about best practices for migrating detection rules.

To migrate your analytics rules to Microsoft Sentinel:

Verify that you have a testing system in place for each rule you want to migrate.
1. Prepare a validation process for your migrated rules, including full test scenarios and scripts.
2. Ensure that your team has useful resources to test your migrated rules.
3. Confirm that you have any required data sources connected, and review your data connection methods.
Verify whether your detections are available as built-in templates in Microsoft Sentinel:
- Use the SIEM migration experience to automate translation and migration.
  
  For more information, see Use the SIEM migration experience.
- If the built-in rules are sufficient, use built-in rule templates to create rules for your own workspace.
  
  In Microsoft Sentinel, go to the Configuration > Analytics > Rule templates tab, and create and update each relevant analytics rule.
  
  For more information, see Detect threats out-of-the-box.
- If you have detections that aren't covered by Microsoft Sentinel's built-in rules, try an online query converter, such as Uncoder.io or SPL2KQL to convert your queries to KQL.
  
  Identify the trigger condition and rule action, and then construct and review your KQL query.
- If neither the built-in rules nor an online rule converter is sufficient, you'll need to create the rule manually. In such cases, use the following steps to start creating your rule:
  1. Identify the data sources you want to use in your rule. You'll want to create a mapping table between data sources and data tables in Microsoft Sentinel to identify the tables you want to query.
  2. Identify any attributes, fields, or entities in your data that you want to use in your rules.
  3. Identify your rule criteria and logic. At this stage, you may want to use rule templates as samples for how to construct your KQL queries.
    
    Consider filters, correlation rules, active lists, reference sets, watchlists, detection anomalies, aggregations, and so on. You might use references provided by your legacy SIEM to understand how to best map your query syntax.
  4. Identify the trigger condition and rule action, and then construct and review your KQL query. When reviewing your query, consider KQL optimization guidance resources.
Test the rule with each of your relevant use cases. If it doesn't provide expected results, you may want to review the KQL and test it again.
When you're satisfied, you can consider the rule migrated. Create a playbook for your rule action as needed. For more information, see Automate threat response with playbooks in Microsoft Sentinel.

Learn more about analytics rules:

Create custom analytics rules to detect threats. Use alert grouping to reduce alert fatigue by grouping alerts that occur within a given timeframe.
Map data fields to entities in Microsoft Sentinel to enable SOC engineers to define entities as part of the evidence to track during an investigation. Entity mapping also makes it possible for SOC analysts to take advantage of an intuitive [investigation graph] (investigate-cases.md#use-the-investigation-graph-to-deep-dive) that can help reduce time and effort.
Investigate incidents with UEBA data, as an example of how to use evidence to surface events, alerts, and any bookmarks associated with a particular incident in the incident preview pane.
Kusto Query Language (KQL), which you can use to send read-only requests to your Log Analytics database to process data and return results. KQL is also used across other Microsoft services, such as Microsoft Defender for Endpoint and Application Insights.

Compare rule terminology

This table helps you to clarify the concept of a rule in Microsoft Sentinel compared to Splunk.

	Splunk	Microsoft Sentinel
Rule type	• Scheduled • Real-time	• Scheduled query • Fusion • Microsoft Security • Machine Learning (ML) Behavior Analytics
Criteria	Define in SPL	Define in KQL
Trigger condition	• Number of results • Number of hosts • Number of sources • Custom	Threshold: Number of query results
Action	• Add to triggered alerts • Log Event • Output results to look up • And more	• Create alert or incident • Integrates with Logic Apps

Map and compare rule samples

Use these samples to compare and map rules from Splunk to Microsoft Sentinel in various scenarios.

Common search commands

SPL command	Description	KQL operator	KQL example
`chart/ timechart`	Returns results in a tabular output for time-series charting.	render operator	`… \| render timechart`
`dedup`	Removes subsequent results that match a specified criterion.	• distinct • summarize	`… \| summarize by Computer, EventID`
`eval`	Calculates an expression. Learn about common eval commands.	extend	`T \| extend duration = endTime - startTime`
`fields`	Removes fields from search results.	• project • project-away	`T \| project cost=price*quantity, price`
`head/tail`	Returns the first or last N results.	top	`T \| top 5 by Name desc nulls last`
`lookup`	Adds field values from an external source.	• externaldata • lookup	KQL example
`rename`	Renames a field. Use wildcards to specify multiple fields.	project-rename	`T \| project-rename new_column_name = column_name`
`rex`	Specifies group names using regular expressions to extract fields.	matches regex	`… \| where field matches regex "^addr.*"`
`search`	Filters results to results that match the search expression.	search	`search "X"`
`sort`	Sorts the search results by the specified fields.	sort	`T \| sort by strlen(country) asc, price desc`
`stats`	Provides statistics, optionally grouped by fields. Learn more about common stats commands.	summarize	KQL example
`mstats`	Similar to stats, used on metrics instead of events.	summarize	KQL example
`table`	Specifies which fields to keep in the result set, and retains data in tabular format.	project	`T \| project columnA, columnB`
`top/rare`	Displays the most or least common values of a field.	top	`T \| top 5 by Name desc nulls last`
`transaction`	Groups search results into transactions. SPL example	Example: row_window_session	KQL example
`eventstats`	Generates summary statistics from fields in your events and saves those statistics in a new field. SPL example	Examples: • join • make_list • mv-expand	KQL example
`streamstats`	Find the cumulative sum of a field. SPL example: `... \| streamstats sum(bytes) as bytes _ total \\| timechart`	row_cumsum	`...\\| serialize cs=row_cumsum(bytes)`
`anomalydetection`	Find anomalies in the specified field. SPL example	series_decompose_anomalies()	KQL example
`where`	Filters search results using `eval` expressions. Used to compare two different fields.	where	`T \| where fruit=="apple"`

lookup command: KQL example

Users 
| where UserID in ((externaldata (UserID:string) [
@"https://storageaccount.blob.core.windows.net/storagecontainer/users.txt" 
h@"?...SAS..." // Secret token to access the blob 
])) | ...

stats command: KQL example

Sales 
| summarize NumTransactions=count(), 
Total=sum(UnitPrice * NumUnits) by Fruit, 
StartOfMonth=startofmonth(SellDateTime)

mstats command: KQL example

T | summarize count() by price_range=bin(price, 10.0)

transaction command: SPL example

sourcetype=MyLogTable type=Event
| transaction ActivityId startswith="Start" endswith="Stop"
| Rename timestamp as StartTime
| Table City, ActivityId, StartTime, Duration

transaction command: KQL example

let Events = MyLogTable | where type=="Event";
Events
| where Name == "Start"
| project Name, City, ActivityId, StartTime=timestamp
| join (Events
| where Name == "Stop"
| project StopTime=timestamp, ActivityId)
on ActivityId
| project City, ActivityId, StartTime, 
Duration = StopTime – StartTime

Use row_window_session() to the calculate session start values for a column in a serialized row set.

...| extend SessionStarted = row_window_session(
Timestamp, 1h, 5m, ID != prev(ID))

eventstats command: SPL example

… | bin span=1m _time
|stats count AS count_i by _time, category
| eventstats sum(count_i) as count_total by _time

eventstats command: KQL example

Here's an example with the join statement:

let binSize = 1h;
let detail = SecurityEvent 
| summarize detail_count = count() by EventID,
tbin = bin(TimeGenerated, binSize);
let summary = SecurityEvent
| summarize sum_count = count() by 
tbin = bin(TimeGenerated, binSize);
detail 
| join kind=leftouter (summary) on tbin 
| project-away tbin1

Here's an example with the make_list statement:

let binSize = 1m;
SecurityEvent
| where TimeGenerated >= ago(24h)
| summarize TotalEvents = count() by EventID, 
groupBin =bin(TimeGenerated, binSize)
|summarize make_list(EventID), make_list(TotalEvents), 
sum(TotalEvents) by groupBin
| mvexpand list_EventID, list_TotalEvents

anomalydetection command: SPL example

sourcetype=nasdaq earliest=-10y
| anomalydetection Close _ Price

anomalydetection command: KQL example

let LookBackPeriod= 7d;
let disableAccountLogon=SignIn
| where ResultType == "50057"
| where ResultDescription has "account is disabled";
disableAccountLogon
| make-series Trend=count() default=0 on TimeGenerated 
in range(startofday(ago(LookBackPeriod)), now(), 1d)
| extend (RSquare,Slope,Variance,RVariance,Interception,
LineFit)=series_fit_line(Trend)
| extend (anomalies,score) = 
series_decompose_anomalies(Trend)

Common eval commands

SPL command	Description	SPL example	KQL command	KQL example
`abs(X)`	Returns the absolute value of X.	`abs(number)`	abs()	`abs(X)`
`case(X,"Y",…)`	Takes pairs of `X` and `Y` arguments, where the `X` arguments are boolean expressions. When evaluated to `TRUE`, the arguments return the corresponding `Y` argument.	SPL example	case	KQL example
`ceil(X)`	Ceiling of a number X.	`ceil(1.9)`	ceiling()	`ceiling(1.9)`
`cidrmatch("X",Y)`	Identifies IP addresses that belong to a particular subnet.	`cidrmatch` `("123.132.32.0/25",ip)`	• ipv4_is_match() • ipv6_is_match()	`ipv4_is_match('192.168.1.1', '192.168.1.255')` `== false`
`coalesce(X,…)`	Returns the first value that isn't null.	`coalesce(null(), "Returned val", null())`	coalesce()	`coalesce(tolong("not a number"),` `tolong("42"), 33) == 42`
`cos(X)`	Calculates the cosine of X.	`n=cos(0)`	cos()	`cos(X)`
`exact(X)`	Evaluates an expression X using double precision floating point arithmetic.	`exact(3.14*num)`	todecimal()	`todecimal(3.14*2)`
`exp(X)`	Returns eX.	`exp(3)`	exp()	`exp(3)`
`if(X,Y,Z)`	If `X` evaluates to `TRUE`, the result is the second argument `Y`. If `X` evaluates to `FALSE`, the result evaluates to the third argument `Z`.	`if(error==200,` `"OK", "Error")`	iif()	KQL example
`isbool(X)`	Returns `TRUE` if `X` is boolean.	`isbool(field)`	• iif() • gettype	`iif(gettype(X) =="bool","TRUE","FALSE")`
`isint(X)`	Returns `TRUE` if `X` is an integer.	`isint(field)`	• iif() • gettype	KQL example
`isnull(X)`	Returns `TRUE` if `X` is null.	`isnull(field)`	isnull()	`isnull(field)`
`isstr(X)`	Returns `TRUE` if `X` is a string.	`isstr(field)`	• iif() • gettype	KQL example
`len(X)`	This function returns the character length of a string `X`.	`len(field)`	strlen()	`strlen(field)`
`like(X,"y")`	Returns `TRUE` if and only if `X` is like the SQLite pattern in `Y`.	`like(field, "addr%")`	• has • contains • startswith • matches regex	KQL example
`log(X,Y)`	Returns the log of the first argument `X` using the second argument `Y` as the base. The default value of `Y` is `10`.	`log(number,2)`	• log • log2 • log10	`log(X)` `log2(X)` `log10(X)`
`lower(X)`	Returns the lowercase value of `X`.	`lower(username)`	tolower	`tolower(username)`
`ltrim(X,Y)`	Returns `X` with the characters in parameter `Y` trimmed from the left side. The default output of `Y` is spaces and tabs.	`ltrim(" ZZZabcZZ ", " Z")`	trim_start()	`trim_start(“ ZZZabcZZ”,” ZZZ”)`
`match(X,Y)`	Returns if X matches the regex pattern Y.	`match(field, "^\d{1,3}.\d$")`	matches regex	`… \| where field matches regex @"^\d{1,3}.\d$")`
`max(X,…)`	Returns the maximum value in a column.	`max(delay, mydelay)`	• max() • arg_max()	`… \| summarize max(field)`
`md5(X)`	Returns the MD5 hash of a string value `X`.	`md5(field)`	hash_md5	`hash_md5("X")`
`min(X,…)`	Returns the minimum value in a column.	`min(delay, mydelay)`	• min_of() • min() • arg_min	KQL example
`mvcount(X)`	Returns the number (total) of `X` values.	`mvcount(multifield)`	dcount	`…\| summarize dcount(X) by Y`
`mvfilter(X)`	Filters a multi-valued field based on the boolean `X` expression.	`mvfilter(match(email, "net$"))`	mv-apply	KQL example
`mvindex(X,Y,Z)`	Returns a subset of the multi-valued `X` argument from a start position (zero-based) `Y` to `Z` (optional).	`mvindex( multifield, 2)`	array_slice	`array_slice(arr, 1, 2)`
`mvjoin(X,Y)`	Given a multi-valued field `X` and string delimiter `Y`, and joins the individual values of `X` using `Y`.	`mvjoin(address, ";")`	strcat_array	KQL example
`now()`	Returns the current time, represented in Unix time.	`now()`	now()	`now()` `now(-2d)`
`null()`	Doesn't accept arguments and returns `NULL`.	`null()`	null	`null`
`nullif(X,Y)`	Includes two arguments, `X` and `Y`, and returns `X` if the arguments are different. Otherwise, returns `NULL`.	`nullif(fieldA, fieldB)`	iif	`iif(fieldA==fieldB, null, fieldA)`
`random()`	Returns a pseudo-random number between `0` to `2147483647`.	`random()`	rand()	`rand()`
`relative_ time(X,Y)`	Given an epoch time `X` and relative time specifier `Y`, returns the epoch time value of `Y` applied to `X`.	`relative_time(now(),"-1d@d")`	unix time	KQL example
`replace(X,Y,Z)`	Returns a string formed by substituting string `Z` for every occurrence of regular expression string `Y` in string `X`.	Returns date with the month and day numbers switched. For example, for the `4/30/2015` input, the output is `30/4/2009`: `replace(date, "^(\d{1,2})/ (\d{1,2})/", "\2/\1/")`	replace()	KQL example
`round(X,Y)`	Returns `X` rounded to the number of decimal places specified by `Y`. The default is to round to an integer.	`round(3.5)`	round	`round(3.5)`
`rtrim(X,Y)`	Returns `X` with the characters of `Y` trimmed from the right side. If `Y` isn't specified, spaces and tabs are trimmed.	`rtrim(" ZZZZabcZZ ", " Z")`	trim_end()	`trim_end(@"[ Z]+",A)`
`searchmatch(X)`	Returns `TRUE` if the event matches the search string `X`.	`searchmatch("foo AND bar")`	iif()	`iif(field has "X","Yes","No")`
`split(X,"Y")`	Returns `X` as a multi-valued field, split by delimiter `Y`.	`split(address, ";")`	split()	`split(address, ";")`
`sqrt(X)`	Returns the square root of `X`.	`sqrt(9)`	sqrt()	`sqrt(9)`
`strftime(X,Y)`	Returns the epoch time value `X` rendered using the format specified by `Y`.	`strftime(_time, "%H:%M")`	format_datetime()	`format_datetime(time,'HH:mm')`
`strptime(X,Y)`	Given a time represented by a string `X`, returns value parsed from format `Y`.	`strptime(timeStr, "%H:%M")`	format_datetime()	KQL example
`substr(X,Y,Z)`	Returns a substring field `X` from start position (one-based) `Y` for `Z` (optional) characters.	`substr("string", 1, 3)`	substring()	`substring("string", 0, 3)`
`time()`	Returns the wall-clock time with microsecond resolution.	`time()`	format_datetime()	KQL example
`tonumber(X,Y)`	Converts input string `X` to a number, where `Y` (optional, default value is `10`) defines the base of the number to convert to.	`tonumber("0A4",16)`	toint()	`toint("123")`
`tostring(X,Y)`	Description	SPL example	tostring()	`tostring(123)`
`typeof(X)`	Returns a string representation of the field type.	`typeof(12)`	gettype()	`gettype(12)`
`urldecode(X)`	Returns the URL `X` decoded.	SPL example	url_decode	KQL example

case(X,"Y",…) SPL example

case(error == 404, "Not found",
error == 500,"Internal Server Error",
error == 200, "OK")

case(X,"Y",…) KQL example

T
| extend Message = case(error == 404, "Not found", 
error == 500,"Internal Server Error", "OK")

if(X,Y,Z) KQL example

iif(floor(Timestamp, 1d)==floor(now(), 1d), 
"today", "anotherday")

isint(X) KQL example

iif(gettype(X) =="long","TRUE","FALSE")

isstr(X) KQL example

iif(gettype(X) =="string","TRUE","FALSE")

like(X,"y") example

… | where field has "addr"

… | where field contains "addr"

… | where field startswith "addr"

… | where field matches regex "^addr.*"

min(X,…) KQL example

min_of (expr_1, expr_2 ...)

…|summarize min(expr)

…| summarize arg_min(Price,*) by Product

mvfilter(X) KQL example

T | mv-apply Metric to typeof(real) on 
(
 top 2 by Metric desc
)

mvjoin(X,Y) KQL example

strcat_array(dynamic([1, 2, 3]), "->")

relative time(X,Y) KQL example

let toUnixTime = (dt:datetime)
{
(dt - datetime(1970-01-01))/1s 
};

replace(X,Y,Z) KQL example

replace( @'^(\d{1,2})/(\d{1,2})/', @'\2/\1/',date)

strptime(X,Y) KQL example

format_datetime(datetime('2017-08-16 11:25:10'),
'HH:mm')

time() KQL example

format_datetime(datetime(2015-12-14 02:03:04),
'h:m:s')

tostring(X,Y)

Returns a field value of X as a string.

If the value of X is a number, X is reformatted to a string value.
If X is a boolean value, X is reformatted to TRUE or FALSE.
If X is a number, the second argument Y is optional and can either be hex (converts X to a hexadecimal), commas (formats X with commas and two decimal places), or duration (converts X from a time format in seconds to a readable time format: HH:MM:SS).

tostring(X,Y) SPL example

This example returns:

foo=615 and foo2=00:10:15:

… | eval foo=615 | eval foo2 = tostring(
foo, "duration")

urldecode(X) SPL example

urldecode("http%3A%2F%2Fwww.splunk.com%2Fdownload%3Fr%3Dheader")

Common stats commands KQL example

SPL command	Description	KQL command	KQL example
`avg(X)`	Returns the average of the values of field `X`.	avg()	`avg(X)`
`count(X)`	Returns the number of occurrences of the field `X`. To indicate a specific field value to match, format `X` as `eval(field="value")`.	count()	`summarize count()`
`dc(X)`	Returns the count of distinct values of the field `X`.	dcount()	`…\\| summarize countries=dcount(country) by continent`
`earliest(X)`	Returns the chronologically earliest seen value of `X`.	arg_min()	`… \\| summarize arg_min(TimeGenerated, *) by X`
`latest(X)`	Returns the chronologically latest seen value of `X`.	arg_max()	`… \\| summarize arg_max(TimeGenerated, *) by X`
`max(X)`	Returns the maximum value of the field `X`. If the values of `X` are non-numeric, the maximum value is found via alphabetical ordering.	max()	`…\\| summarize max(X)`
`median(X)`	Returns the middle-most value of the field `X`.	percentile()	`…\\| summarize percentile(X, 50)`
`min(X)`	Returns the minimum value of the field `X`. If the values of `X` are non-numeric, the minimum value is found via alphabetical ordering.	min()	`…\\| summarize min(X)`
`mode(X)`	Returns the most frequent value of the field `X`.	top-hitters()	`…\\| top-hitters 1 of Y by X`
`perc(Y)`	Returns the percentile `X` value of the field `Y`. For example, `perc5(total)` returns the fifth percentile value of a field `total`.	percentile()	`…\\| summarize percentile(Y, 5)`
`range(X)`	Returns the difference between the maximum and minimum values of the field `X`.	range()	`range(1, 3)`
`stdev(X)`	Returns the sample standard deviation of the field `X`.	stdev	`stdev()`
`stdevp(X)`	Returns the population standard deviation of the field `X`.	stdevp()	`stdevp()`
`sum(X)`	Returns the sum of the values of the field `X`.	sum()	`sum(X)`
`sumsq(X)`	Returns the sum of the squares of the values of the field `X`.
`values(X)`	Returns the list of all distinct values of the field `X` as a multi-value entry. The order of the values is alphabetical.	make_set()	`…\\| summarize r = make_set(X)`
`var(X)`	Returns the sample variance of the field `X`.	variance	`variance(X)`

Next steps

In this article, you learned how to map your migration rules from Splunk to Microsoft Sentinel.

Migrate your SOAR automation