SNORT Rule Tuning Guide: Reducing False Positives Without Losing Visibility

Sort your last month of SNORT alerts by signature and count them. On most deployments running a community ruleset unmodified, somewhere between five and fifteen SIDs account for the large majority of everything the sensor produced — and the top one is almost always a scanner you own, a load balancer health check, or ET POLICY firing on traffic that is entirely expected in your environment.

That distribution is the whole problem in one chart. It also means the first week of tuning is not hard: a handful of correct decisions removes most of the volume. What is hard is the second year, when the easy wins are gone, forty suppressions are in place, nobody remembers why eleven of them exist, and the question is no longer “how do we reduce alerts” but “what have we stopped seeing”.

The failure mode to avoid is subtler than an overloaded queue. An analyst who has closed the same alert as benign two hundred times will close the two hundred and first the same way in four seconds, without opening the packet. They are not being lazy — they have correctly learned the base rate, and this alert has taught them it is noise. The detection is now worse than absent, because it occupies triage capacity while guaranteeing that the one true positive hiding in that signature gets closed on autopilot. Tuning is how you stop training your analysts to ignore things.

Building a practical SNORT tuning program

Tuning is detection engineering, which means it needs the things engineering needs: a baseline, change records, a way to test before production, and someone whose job it is. Run it as a series of urgent fixes and you get a queue that is quiet and a sensor nobody can describe the coverage of.

Why tuning matters more than most teams realise

The degradation is invisible on every dashboard you have. Alert volume looks stable. Mean time to close looks good — improving, even — because closing known noise fast is exactly what an experienced analyst does. Nothing in your metrics distinguishes a team triaging effectively from a team pattern-matching on signature names, and the second one is cheaper to produce.

What it costs shows up somewhere else entirely: in the post-incident review, where the timeline shows the relevant alert fired on day three and was closed in six seconds on day three. That is the outcome poor tuning buys, and it arrives as an incident rather than as a metric.

Tuning done properly gets you:

Analysts spending their attention on alerts where investigation changes the answer
Faster triage that is fast because the alert was clear, not because it was familiar
Better escalations, since an alert that survived tuning carries context worth passing on
A detection programme you can describe — which SIDs are enabled, which are suppressed, why, and by whom
Preserved visibility on the assets where a miss is expensive, which is where the tuning bar should be highest

The point of the exercise is not a shorter queue. It is that when something fires, an analyst believes it enough to look.

The visibility trade-off you need to understand

Disabling a rule is the fastest fix available and it is measured by a metric that immediately rewards you. Queue length drops, the analyst who raised it is happy, and the change takes thirty seconds. There is no feedback loop anywhere in that sequence that tells you what you gave up, and none will arrive for months.

That asymmetry is why disabling is the default and why mature teams treat it as the option of last resort. Note what the right-hand column below has in common: every alternative costs more effort up front and keeps the detection alive in some narrower form. That is the actual trade — engineering time now against coverage you cannot measure the loss of later.

Tuning Choice	Short-Term Effect	Long-Term Risk	Better Alternative
Disable entire rule category	Immediate alert drop	Loss of attack-surface coverage	Threshold tuning + context filtering
Broad suppression by source range	Fewer repeated alerts	Masks compromised internal hosts	Narrow suppression with expiration dates
Ignore low severity globally	Reduces queue size	Misses chained low-to-high kill paths	Risk-based correlation and escalation rules
Keep all defaults unchanged	Preserves raw visibility	Analyst burnout and missed true positives	Structured baseline and phased tuning

The second row deserves particular attention, because suppressing by internal source range is the most common and most dangerous shortcut in the list. A suppress on your workstation subnet for an exploit signature does not just silence the false positives from your patch management agent — it silences the same signature when a compromised laptop on that subnet starts scanning internally. You have removed detection precisely in the zone where lateral movement happens. If you must do it, scope it to the specific source host and destination, not the range, and put an expiry date on it.

The right target isn’t “fewer alerts”. It’s “alerts an analyst can act on”. Those produce similar-looking numbers in the first month and completely different outcomes in the first incident.

Know your environment before you touch anything

Every tuning decision is a judgement about whether observed traffic is expected. You cannot make that judgement without knowing what “expected” is here, and an analyst who does not know that your vulnerability scanner runs from 10.20.4.0/24 every Sunday at 02:00 has two options: escalate a scan every week, or learn to ignore scan alerts entirely. Both are bad, and the second one is what actually happens.

Before adjusting a single threshold, get answers to the following — and write them down somewhere an analyst on shift at 3 a.m. can find them, because tribal knowledge held by one senior engineer is not environmental context, it is a single point of failure:

Context you need to gather:

Network zones and where the trust boundaries sit
Critical assets and crown-jewel services, including who owns them
Normal protocol and traffic profiles by zone
Business hours, maintenance windows, and overnight batch jobs
Approved vulnerability scanners and their source ranges and schedules
Change control calendar for infrastructure and application updates
Current SOC escalation thresholds and triage expectations

Context Area	What to Capture	Why It Matters
Zone model	Internal, DMZ, cloud, partner links	The same alert carries different risk depending on where it fires
Asset criticality	Business impact and service ownership	Drives prioritisation and response urgency
Normal traffic patterns	Baseline ports, protocols, and volumes	Lets you distinguish routine traffic from genuine anomalies
Approved scanners	Source ranges and scan windows	Keeps scanner noise from polluting the analyst queue
Change events	Deployments, migrations, patch windows	Prevents false alert spikes during planned activity

One item on that list is worth more than the rest combined: sensor placement, and specifically whether the sensor sees what you think it sees. A tap on the wrong side of a NAT boundary reports every internal host as one address, which makes per-source suppression meaningless and per-source thresholding actively harmful. A sensor downstream of TLS termination sees plaintext; one upstream sees nothing useful for content rules and will never fire the HTTP signatures you are counting on. And a SPAN port that has been quietly dropping frames under load since a switch upgrade produces a sensor that reports healthy and detects a fraction of what it should — check snort’s own drop counters before you trust any baseline built from its output.

Get this wrong and everything downstream is tuning against a distorted picture of the network.

The tuning workflow that actually holds up over time

The ordering here is not arbitrary. Steps 3 and 5 are the ones teams skip under pressure, and they are the two that prevent the failures that cost you later.

Baseline your current alerts — Two to four weeks broken down by SID, source, destination and zone. Two weeks is the minimum that captures a weekly maintenance cycle; if your monthly patch window is the noisiest night of the month, a fortnight of data will not show it to you. Capture the distribution, not just the totals — a SID firing 5,000 times from one host is a different problem from the same count spread across 300 hosts, and they need opposite fixes.
Classify noise versus potential signal — Three buckets: confirmed benign, unknown, and actionable. The unknown bucket is the one that matters and the one that gets emptied dishonestly, because “I could not explain it and it looked boring” is not the same as benign. Leave it unknown and come back to it.
Validate before you suppress — Correlate against firewall, endpoint, proxy and server logs and confirm the traffic is what you assume it is. This is the step that catches the genuinely bad outcome: the “noisy scanner” that turns out to be an unmanaged host nobody owns, sweeping your internal range on a schedule. It looked periodic and predictable, which is exactly why it read as benign. Ask who owns the source, not just whether the pattern is regular.
Adjust thresholds and detection context — Prefer threshold/event_filter and suppress with narrow scoping over editing rule content, and prefer editing content over disabling. Understand the difference before you choose: event_filter reduces how often you are told while the rule still evaluates, whereas suppress stops evaluation for the matching source or destination entirely. The first is rate control. The second is a blind spot with a defined shape.
Document every suppression with an owner and an expiry date — Non-negotiable, and the reason is specific: suppressions outlive the conditions that justified them. The scanner gets decommissioned, the application is rewritten, the subnet is re-purposed for something sensitive — and the suppression written for the old world is still in the config, now covering traffic nobody has looked at in two years. An undated suppression is permanent by default.
Check coverage impact before deploying — Ask the question explicitly: after this change, what would still fire if this attack came from a host inside the suppressed range? If the answer is “nothing”, you need a compensating detection elsewhere before the change ships, not after.
Roll out in a controlled way — Validate against replayed traffic on a staging sensor, or on one production segment first. snort -T -c catches syntax errors and a rule that loads cleanly can still match nothing at all, so a config that parses is not a change that works.
Monitor and iterate weekly — Watch for the signal that a change went too far: a SID that used to fire daily and now fires never. That is either a successful tune or a broken rule, and the two are indistinguishable without looking.

The rule lifecycle every mature detection team uses

Most rulesets are append-only in practice. Rules get added when a new threat appears and essentially never removed, because removing one requires someone to argue that a detection is no longer needed — and nobody wants that decision attached to their name. The result is a config carrying signatures for products the organisation retired in 2019, each consuming inspection budget and contributing alerts nobody can interpret.

The stage that does the real work below is Retire. Everything else is bookkeeping around it.

Lifecycle Stage	Purpose	Owner	Output
New rule	Introduce a signature into the monitoring set	Detection engineer	Rule metadata and deployment note
Observe	Watch alert behaviour in real traffic	SOC analyst	Baseline alert profile
Tune	Adjust threshold, suppression, or content	Detection engineer and SOC	Tuning change record
Validate	Confirm true-positive preservation and FP reduction	SOC lead	Validation report
Deploy	Promote tuned rule to production baseline	Platform owner	Approved release entry
Review	Periodic performance and relevance check	SOC and threat team	Rule performance scorecard
Retire	Remove obsolete or redundant rules	Detection governance owner	Retirement note and coverage mapping

One caveat on the owner column: in a five-person SOC these are four names, not seven roles, and pretending otherwise produces a process document nobody follows. The distinction that has to survive scaling down is that the person who writes the tune is not the only person who approves it — self-approved suppressions are how coverage disappears quietly. Everything else can collapse into one job.

Retirement also needs a coverage note attached, because “we removed this rule” and “we no longer detect this technique” are different statements, and only the second one requires action.

What analysts should capture on every alert review

Consistent capture is worth the small tax per alert for one reason: it makes the queue searchable. A month of free-text closure notes is unusable, so when you need to answer “have we seen this SID from an external source before, and what did we conclude?” the honest answer is that nobody knows and the investigation starts from zero. Structured fields turn 400 closed tickets into evidence.

Automate whatever the SIEM can fill in. Every field below that an analyst has to type by hand is a field that will be blank by the end of a busy shift, and a mandatory-but-empty field is worse than no field because it looks complete in a report.

Field	Why It Matters
Rule SID and signature name	Identifies rule behaviour and tuning lineage
Timestamp in UTC	Enables cross-system timeline alignment
Source IP and port	Supports source profiling and campaign tracking
Destination IP and port	Links to asset criticality and service context
Protocol	Distinguishes expected from suspicious communication patterns
Zone and sensor location	Adds trust-boundary context to every alert
Alert count and rate	Helps identify burst anomalies and persistently noisy rules
Action taken	Documents the triage path and containment relevance
Correlated telemetry references	Supports confidence scoring and escalation quality

Minimum triage note format:

Rule SID and brief alert summary
Asset criticality and owner
Which correlated logs were reviewed
Confidence level — low, medium, or high
Escalation rationale or closure reasoning

The confidence level is the field to insist on, and it should be allowed to be low. An analyst who can close a ticket as “benign, low confidence, could not fully verify” gives you something genuinely valuable: a list of the alerts your team cannot resolve with the telemetry available. That list is your detection gap, written by the people who hit it daily. A workflow that only permits confident closures throws that information away and gets you certainty that was never real.

Integrating SNORT with Splunk, ELK, and Wazuh

A SNORT alert on its own tells you a packet matched a pattern. It cannot tell you whether the destination is a decommissioned test box or the payroll database, whether the source authenticated successfully thirty seconds earlier, or whether the process on the receiving host then spawned a shell. Those are the facts that decide whether the alert matters, and none of them are in the alert.

This is also where a large share of “false positives” turn out not to be. An exploit signature firing against a host that was never running the affected software is a true detection of a real attempt with no impact — which is a different disposition from a rule that matched benign traffic, and it needs a different response. Without asset context an analyst cannot tell those apart, so both get closed as noise and one of them was a genuine attempt you have now stopped counting.

What integration should achieve:

Centralised alert visibility and deduplication
Correlation with authentication, endpoint, and firewall logs
Risk scoring based on asset criticality and business context
Faster incident handoff with linked evidence already assembled

Platform	Practical Use	Key Benefit
Splunk	Alert aggregation, correlation searches, and dashboards	Faster triage and trend visibility across time
ELK	Flexible enrichment pipelines and threat hunt pivots	Better investigation context and retention control
Wazuh	Host and network correlation for endpoint-IDS alignment	Higher confidence in escalation decisions

High-value correlation examples:

SNORT auth-probe alerts correlated with identity system failed-login spikes
SNORT web exploit patterns correlated with WAF block and allow behaviour changes
SNORT suspicious outbound traffic alerts correlated with endpoint process anomalies

Two practical warnings before you build any of this. Timestamps must be UTC and the sensor’s clock must be right — a sensor forty seconds out of sync produces correlation rules that silently match nothing, and the searches return zero results rather than an error, which reads exactly like “no attacks”. And correlation that requires both sides to be present will fail closed the moment one log source stops arriving, so alert on ingestion volume per source as well as on the correlations themselves. A broken pipeline looks identical to a quiet network on every dashboard you have.

Metrics that tell you whether tuning is working

Every metric here is gameable, and the ones measuring volume are gameable by doing the wrong thing. Disable half the ruleset and alert volume falls, false-positive rate falls, mean time to triage falls, and true-positive rate rises — four green arrows for an action that reduced your coverage. Report these numbers in isolation to a manager who wants improvement and you have built an incentive to blind the sensor.

The guard against that is the coverage row, which is the only metric on this list that moves in the opposite direction when you over-tune. Never present the volume metrics without it.

Metric	Definition	Target Direction
Alert volume	Total IDS alerts in a given period	Decrease carefully — not at the cost of coverage
True-positive rate	Confirmed incidents divided by investigated alerts	Increase
False-positive rate	Non-actionable alerts divided by investigated alerts	Decrease
Mean time to triage	Average analyst time to classify an alert	Decrease
Coverage by asset	Percentage of critical assets with meaningful IDS visibility	Increase
Suppression review compliance	Percentage of suppressions reviewed before expiry	Increase

Two of these are harder to measure than the table implies, and it is worth being honest about that rather than reporting a confident number you cannot defend.

False-positive rate depends on analysts labelling dispositions consistently, which they will not do without a shared definition — decide explicitly whether “attack attempt against a non-vulnerable host” counts as a false positive, because different analysts will answer that differently and your trend line is then measuring labelling drift rather than detection quality.

True-positive rate has a nastier problem: the denominator only contains alerts you investigated, so it says nothing whatsoever about what the sensor never fired on. A programme can hold a 95% true-positive rate while missing most of what happens on the network. Pair it with something that samples the other direction — a purple-team test, an atomic technique replayed against a monitored segment — or the number is reassurance rather than measurement.

Tie every significant movement to a specific change record. A metric that moved for reasons nobody can reconstruct is a graph, not evidence.

Mistakes that quietly undermine a tuning programme

None of these look like mistakes at the moment they are made. Each one is a reasonable decision taken under time pressure by someone competent, and the damage is only visible in aggregate a year later — which is precisely why guardrails have to be structural rather than a matter of individual discipline.

Common errors to watch for:

Suppressing too broadly just to reduce queue pressure
Letting suppressions persist without an owner or expiration date
Ignoring asset criticality when setting or adjusting thresholds
Failing to retest rules after infrastructure changes
Tuning in production without any staged validation
Treating approved scanner noise as a permanent baseline behaviour
Not documenting why rule changes were made — future teams pay for this

The guardrails that prevent most of these problems:

No suppression without a reason, an owner, and a review date
No category-wide disable without a coverage impact review
No threshold change without a before-and-after metric snapshot

Three rules is about the limit of what a team under pressure will actually follow. Add a fourteen-step approval workflow and you get tuning done quietly outside the process, which is strictly worse than a light process followed consistently.

Make them cheap to comply with. If recording a suppression means a comment in the version-controlled config with a name and a date — which the config file already needs anyway — compliance is nearly free. If it means a change ticket, two approvals and a CAB slot, the suppression will be made in production first and documented never.

That points at the highest-leverage control here, and it is not on the list above: put your rule and suppression configuration in Git. Every change gets an author, a timestamp, a diff and a message for free, and git log answers “when did this suppression appear and who added it” without anyone having to maintain a separate register.

The monthly tuning cadence that mature SOCs use

The value of a fixed cadence is not the schedule itself — it is that tuning gets time that is not stolen from triage. Detection engineering done in the gaps between alerts is done badly, because the gaps are where an analyst is recovering. Book the hours.

Scale this to what you have. A four-person SOC will not run a four-week cycle with distinct deliverables; a half-day a month spent on the top five noisy SIDs and a review of expiring suppressions captures most of the value. The parts that do not compress are the baseline review at the start and the impact check at the end.

Week	Focus	Deliverable
Week 1	Baseline and trend review — focus on top noisy SIDs with context tags	Top-noise report and asset criticality map
Week 2	Validate suspected false positives through log correlation	FP validation log
Week 3	Apply controlled threshold and suppression adjustments	Tuning change set with approvals
Week 4	Measure impact and review missed-signal risk	Monthly tuning scorecard

Monthly governance checklist:

Top 20 noisiest rules reviewed
All active suppressions have an owner and expiry date
Critical asset coverage verified after any changes
True-positive and false-positive rates updated
Incident lessons fed back into rule improvements
Next month’s tuning priorities documented

The item most likely to be skipped is the second checkbox, and it is the one that compounds. Suppressions without a review date accumulate at maybe two or three a month, which sounds harmless until you are forty deep and the config describes a network that no longer exists.

A mature SNORT programme is not a quiet one. It is one where somebody can answer, from records rather than memory, what the sensor is watching, what it has been told to ignore, and who decided.

Acceptance criteria for tuned rules

Without a shared bar, “tuned” means whatever the person who made the change thought was good enough, and the standard drifts with workload — looser in a busy quarter, tighter after an incident. Fixed criteria make the decision reviewable by someone who was not there.

The first row carries more weight than it appears to. If an analyst cannot tell from the alert what behaviour was observed, no amount of threshold tuning helps: they still have to open the packet capture every time, which is the actual cost driver in triage. Sometimes the right fix for a noisy rule is a clearer msg field and a reference URL, not a suppression.

Acceptance Check	Pass Condition
Signal quality	Alert explains the observed behaviour clearly enough for first-pass triage
Correlation readiness	Alert contains fields that can link to SIEM and endpoint data
Noise tolerance	False-positive rate stays within the team’s defined threshold
Critical coverage	No loss of visibility on critical assets or high-risk zones
Documentation	Rule purpose, tuning reason, and owner are all recorded

If a tuned rule fails two or more of these, roll it back. Rolling back is cheap when the config is in version control and expensive when it is a file somebody edited in place three weeks ago — which is the practical argument for Git that no amount of policy language makes as well.

Quarterly governance: preventing long-term detection drift

The monthly cycle looks at alerts, which means it can only see rules that fire. The drift that hurts is in the rules that do not — a signature covering a technology you decommissioned, a sensor watching a segment that was re-architected six months ago, a detection for a technique the threat groups relevant to you stopped using. None of that generates a single alert to notice, so it survives every monthly review indefinitely.

Quarterly review is where you go looking for silence and ask whether it is earned.

Quarterly governance actions:

Revalidate rule relevance against current threat intelligence and attack trends
Review suppressions that have exceeded their intended lifetime
Reassess sensor placement against infrastructure changes made since the last review
Compare IDS coverage against vulnerability scan findings and recent incident patterns
Update rule ownership for services that have been retired or transferred

Governance Domain	Key Question
Coverage	Are critical assets still mapped to effective IDS visibility?
Quality	Are the top noisy rules improving quarter over quarter, or recurring unchanged?
Ownership	Does every high-impact rule have an accountable owner?
Responsiveness	Are lessons from incidents being converted into rule improvements quickly?

Add one question the table does not ask: which of our rules have fired zero times this quarter? Some of those are correct — a signature for an attack nobody attempted is doing its job by staying quiet. Others are broken, watching a decommissioned segment, or sitting behind a suppression somebody forgot. You cannot tell which from the alert count, and the only way to find out is to test a sample of them deliberately.

Detection stays healthy when rule engineering, analyst feedback and governance operate as one loop. It decays when they are three separate documents owned by three people who meet quarterly.

Treating tuning like engineering: change control and test harnesses

Detection rules are production code that happens to live in a .rules file. They have inputs, logic, versions, and consumers who are affected when they break — and they are routinely edited live, on the sensor, at 11 p.m., by whoever was on shift when the queue got bad. No other production system in the organisation is changed that way.

Everything in this section is the discipline you would apply to any other deployed artefact, and none of it is exotic. Config in Git. A record of why. A test before it ships.

Minimum rule change record:

Field	Example
Rule SID	`1:2024210`
Change type	Threshold / suppress / content update
Reason	“High-volume benign scanner from approved subnet during weekly scan window”
Evidence	Alert samples, relevant packet extracts, affected timeframe
Risk	What could be missed after the change
Reviewer	Name and team

The Risk row is the one that earns its place. Writing down what could now be missed forces the thought that the thirty-second fix skips, and it gives the next reviewer something to check against. “Unknown” is an acceptable answer; a blank field is not.

Building a basic test harness:

Keep two PCAP sets, not one. Benign captures representing your normal traffic prove the change silenced what you meant to silence; malicious captures for your must-detect scenarios prove it did not silence anything else. Almost everyone builds the first set and skips the second, which is why over-tuning goes unnoticed.
Replay with snort -r capture.pcap against a staging config for anything touching a high-severity signature. This is minutes of work and catches the case where a broadened suppress quietly covers an exploit attempt as well as the scanner it was written for.
Sources for the malicious side: Malware-Traffic-Analysis.net, your own past incident captures (the most valuable set you have, and the one most teams delete), and traffic generated by Atomic Red Team on an isolated segment.
Store the PCAPs alongside the rules in version control if size permits, or reference them by path and checksum. A harness nobody can locate is not a harness.

Performance and stability metrics to track alongside quality:

One belongs at the top of this list and is missing from most dashboards: packet drop rate. A sensor dropping 30% of traffic is not detecting 70% of attacks — it is detecting an unpredictable subset, missing reassembly on the flows it half-saw, and reporting itself entirely healthy the whole time. Snort logs its drop statistics on exit and via perfmonitor; graph that number next to alert volume, because a sudden fall in alerts after a traffic increase is far more likely to be a saturated sensor than a quiet week.

Metric	What It Shows
Alerts per 1,000 packets	Tracks noise relative to traffic volume
CPU and memory per sensor	Prevents performance regressions from new or modified rules
Top noisy signatures	Focuses tuning effort where it has the most impact
Suppression count over time	Early signal of drift and overfitting

One distinction worth keeping clear, because it is the single most common category error in tuning work. Three different problems produce an identical symptom — a noisy, unhelpful alert queue — and they have three different fixes:

The traffic is expected. Suppress or threshold it, narrowly, with an owner and an expiry date. This is the only case suppression is for.
The pipeline is broken. Truncated payloads, a parser mangling fields, a sensor seeing one side of the conversation. Fix the ingestion; suppressing here hides a data-quality fault that is degrading every other rule at the same time.
The rule is wrong. The logic matches things it was never meant to match. Rewrite it — tighten the content match, add a flow constraint, scope it to the right ports. Suppressing a broken rule leaves the broken logic in place and adds a blind spot on top.

Suppression is the fix for the first case only. Used on the other two it converts a diagnosable problem into a permanent, undocumented gap, and the queue gets quiet enough that nobody revisits it.

90-day plan for teams building a tuning programme from scratch

Starting from nothing, this is a realistic three months of part-time effort — call it a day a week — rather than a project plan. Do it in this order specifically: the temptation is to start with the noisy rules because that is where the pain is, and validating the sensor first is what stops you tuning against bad data for a month.

Days 1–30: Build the baseline

Verify the sensor before you trust its output: check packet drop counters, confirm what the tap or SPAN actually sees, confirm the clock is synchronised. If drops are significant, that is the first month’s work and tuning waits.
Get the rules and suppression config into Git on day one. It costs an hour and it is the thing you cannot retrofit — three months of undocumented changes are gone for good.
Capture baseline alert data by SID, zone, source and destination, and write down the environmental context — zones, scanner ranges, maintenance windows — while you are gathering it.
Fix the top three noise sources only. Narrow scope, documented, expiry dates. Resist doing twenty.
Publish an imperfect scorecard. The first one establishes that the numbers exist and are read; accuracy improves from there.

Days 31–60: Expand and integrate

Get asset criticality into the alert pipeline. This is the enrichment with the highest return, because it is what lets an analyst distinguish an attempt against a patched test box from the same attempt against a production database — and it changes triage on every alert, not just tuned ones.
Build the malicious-PCAP half of the test harness and replay it against your current config. Expect to find at least one thing you already broke in month one. Everyone does; the point is finding it in month two rather than during an incident.
Audit existing suppressions: name an owner and a date for every one, and delete any nobody can justify. Deleting a suppression is cheap and reversible; that is exactly why this is the right moment to be aggressive about it.
Start tracking true-positive lift, with the caveat above that the denominator excludes everything the sensor missed.

Days 61–90: Govern and sustain

Run the first quarterly review, and include the zero-alert question — which enabled rules have never fired, and are they silent for a good reason.
Validate a sample of must-detect scenarios end to end, from replayed traffic through the SIEM to an alert in front of an analyst. Every stage in that chain can fail silently, and testing only the rule tests the least fragile link.
Update the standards from what the first two months actually taught you, not from what the plan said they would.
Publish next quarter’s priorities and, more importantly, name who owns tuning. A programme with a documented cadence and no owner reverts to ad hoc within a quarter of whoever set it up moving on.

KPI	Why It Matters
False-positive reduction rate	Measures tuning effectiveness directly
True-positive confirmation rate	Ensures detection value hasn’t been traded away
Suppressions with valid owner and expiry	Reflects governance maturity
Coverage against critical assets	Guards against blind spots created by tuning

Read that table as two pairs pulling against each other. The first two metrics are in tension by design — drive false positives down hard enough and you take true positives with them — and the second two are the audit that catches you doing it. Report any one of them alone and you will optimise for the wrong thing.

The honest summary of IDS tuning is that it never finishes and it is never dramatic. The wins are a slightly shorter queue, a suppression removed before it aged into a blind spot, an analyst who opened the packet capture because the alert had earned it. Do that consistently for a year and you have a sensor whose coverage somebody can describe. Skip it for a year and you have a quiet queue and no idea what you are missing — which, from the dashboard, looks like exactly the same thing.