An AI alternative to SOAR changes how alerts get investigated, swapping fixed playbooks for agents that reason through each one on their own. Which alerts exist to investigate is settled upstream, before any agent runs. Agents only see the alerts your SIEM generates, and your SIEM only generates them for the log sources it ingests. Every source a team drops at ingestion to reduce storage costs stops producing alerts, so neither agents nor analysts are notified when something goes wrong. Reasoning faster over the data you already collect leaves that blind spot exactly where it was.
Start with what the label actually means. An AI alternative to SOAR replaces static, pre-written playbooks with LLM-driven agents that investigate alerts on their own and adapt to threats for which no response was scripted. The ones that hold up in production start with complete log coverage.
That gap matters because the structural problem with SOAR was never the playbook model itself. Playbooks are slow to build and brittle to maintain, and both are worth fixing. Underneath them sits the harder problem. SOAR inherited whatever partial visibility the underlying SIEM had, and it automated within that limit. Layer agents onto the same incomplete foundation, and the reasoning gets sharper while the blind spots stay exactly where they were.
SOAR was built to orchestrate responses to alerts the SIEM had already produced. When an endpoint detection tool fires, SOAR runs a playbook to check reputation, isolate the host, open a ticket, and notify the analyst. The automation works, and the time savings hold up for the subset of alerts it handles well.
The constraint that SOAR never resolved is structural. According to the 2024 SANS SOC Survey cited by Activant Capital research, automation currently handles 17% of security alerts across deployed SOC tooling, while 67% of all alerts are ignored entirely. SOC teams average 4,500 alerts per day, and at Fortune 100 organizations, the number runs into the millions. SOAR never closed this gap. It automated the alerts that fit a predefined response template, and everything else went to the queue, where most of it stayed uninvestigated.
The second constraint is playbook maintenance. Strike48's analysis of SOAR tooling puts the growth in operational overhead at roughly 15 to 20% annually. APIs change, and integrations break silently, and attack patterns keep moving past what the original playbooks were written to handle. Teams running mature SOAR deployments often dedicate nearly half a full-time engineer to keeping existing playbooks functional, diverting engineering capacity that could go to threat response to upkeep instead.
Neither of these is a reasoning problem, which is the trap most buyers walk into. An AI alternative to SOAR that swaps playbooks for LLM-driven agents does not fix a 67% ignore rate when the underlying alert stream is incomplete.
Every agentic platform in this category differs from legacy SOAR in ways that matter.
A coordinator agent can split an investigation and hand the pieces to specialist agents. The system adapts to alert patterns no one wrote a playbook for, and the investigation output is richer than any static, rule-based playbook produces. Set against a hand-authored playbook, that is a real step forward.
They also share one assumption with legacy SOAR, and it is easy to miss. Both take for granted that the log data feeding them is complete. They sit atop existing SIEM infrastructure and optimize what the SIEM produces, so when the SIEM monitors only a fraction of the environment because log storage economics forced coverage trade-offs, the AI platform inherits every one of those gaps. The agents never fire on the excluded log sources because those sources produce no alerts, and no alerts mean no investigation ever starts. The same ceiling applies to XDR. Consolidating detection across endpoint, network, and identity tightens correlation, but it still only sees the sources feeding it, so the coverage economics that shrank visibility in the first place go untouched.
Strike48's 2026 survey of 100 security leaders puts numbers on it. 84% say their current tools cannot access all their log data for investigations, and 65% have had at least one investigation stall because data sat in a system their tools could not reach. These are the operational norms that every AI-over-SIEM platform inherits as a starting condition, so faster investigation of the alerts you already get is a real improvement, and it still leaves you blind to the attack surface that never generates an alert in the first place.
A genuine AI alternative to SOAR requires two conditions to hold at once, and the order matters. Complete log coverage must precede agentic investigation. An AI platform that nails the second and skips the first just gets to the wrong answer sooner.
Coverage decoupled from storage cost.
Strike48 addresses this through a federated search architecture that decouples coverage decisions from storage costs. Traditional SIEMs force teams to decide at ingestion time which log sources to parse and retain, creating budget-driven blind spots before a single alert has been investigated. Federated search stores everything raw and reads a source only when a query actually runs against it. Coverage decisions become risk decisions, and every log source the team connects becomes immediately visible to agents.
Search-in-place connectors that eliminate migration friction. Connectors for S3, Splunk, Elastic, and existing data lakes let Strike48 query logs where they already live, with no rip-and-replace, no duplicate storage costs, and no deployment window measured in months before the first investigation runs. Smart collection covers roughly 80% of log sources in under a day when normalization or speed makes centralization worthwhile.
Once the data foundation is fully complete, the micro-agent architecture can perform the investigative work that SOAR could never handle. A coordinator agent receives the alert, splits it into specific, bounded tasks (check these IPs, pull this user's authentication history, run this behavioral baseline), and routes each task to a specialist agent with a defined knowledge graph and a constrained toolset, so no single agent carries an overloaded mandate. GraphRAG persona graphs define what each agent knows and how it reasons, and MCP (Model Context Protocol) tool constraints define what actions each agent is permitted to take.
The investigation outputs reflect the actual environment. That grounding is what makes Strike48's agent outputs trustworthy. You can trace any verdict back to the specific log lines that produced it. In early Strike48 deployments, mean time to detection dropped below eight minutes, and agents uncovered active phishing campaigns that legacy SIEM tooling had missed entirely.
When evaluating an AI alternative to SOAR, the questions worth asking dig into prerequisites before they ever touch the feature list.
| The question to ask | A weak answer sounds like | A strong answer sounds like |
|---|---|---|
| Which log sources does the platform actually cover, and how was that number set? | A per-GB ingestion rate, or a parsing decision locked in at onboarding. Coverage is capped by budget. | Coverage is driven by risk rather than storage cost. Every connected source is queryable, priced independently of how much you retain. |
| When an agent investigates an alert, what limits what it can know and do? | One general model pointed at everything, producing statistically plausible output that still needs a human to verify it. | A scoped agent with a defined knowledge graph and a fixed toolset, producing output tied to your actual environment. |
| Where exactly does a human approve an agent's action, and why there? | Either full autonomy with no gates, or approval demanded at every step so nothing moves faster than before. | Autonomy for investigation and triage, human sign-off at defined gates for high-consequence actions like host isolation or account lockout. |
All three point in the same direction, away from the agents themselves. Each one checks whether the platform has fixed the coverage and scoping problems sitting underneath the agents, since that is where SOAR broke.
As an AI alternative to SOAR, Strike48 gives security teams a complete agentic log intelligence platform that addresses SOAR's failure modes at the root. The data foundation layer connects to existing log stores via search-in-place connectors, making complete coverage economically viable through federated search. The agentic layer runs Tier 1 and Tier 2 investigations autonomously, with specialist agents handing off work to one another, as in a coordinated SOC team. Pre-built agent packages cover alert triage, phishing investigation, root cause analysis, and evidence collection, and Prospector Studio lets teams build custom agents for workflows beyond those packages without requiring dedicated AI engineering resources.
The payoff shows up in what the agents can actually reach. Strike48 agents run investigations, correlate alerts across the entire log environment, identify patient zero, collect evidence with a verifiable audit trail, and escalate only confirmed, high-confidence threats for human review. Every agent action leaves a verifiable audit trail, so a team can trace an investigation's conclusion back to the specific log data behind it during a post-incident review. In early customer deployments, analysts have reported saving roughly 30 minutes a day on routine investigation work and have stopped managing a queue that never clears.
If your current SOAR setup forces coverage trade-offs or your AI pilots have not reduced human workload, see what an AI alternative to SOAR looks like when it starts with complete visibility rather than layering agents.
SOAR is being displaced for a growing share of the work rather than switched off overnight. The playbook model still runs responses for well-defined alerts, but it never handled the majority of the queue, and teams are moving that unhandled work to agent-driven platforms that investigate without a pre-written script. Whatever replaces it starts from complete log coverage and reasons over the whole picture, which a faster playbook engine could never do.
A SIEM collects and stores log data and generates alerts from it. SOAR sits on top of the SIEM and automates responses to those alerts using playbooks. XDR consolidates detection across endpoints, networks, and identity into a single correlated layer. All three depend on the same underlying input, the log sources actually feeding them. When coverage is partial, faster correlation and faster response both operate over an incomplete picture.
It does not have to. Search-in-place connectors let an agentic platform query logs where they already live, in S3, Splunk, Elastic, or an existing data lake, with no rip-and-replace and no duplicate storage bill. The point is to reach the log sources the SIEM left out for cost reasons, without forcing another migration before the first investigation runs.
Not on their own. An assistant that summarizes cases or drafts playbook steps makes a SOAR platform easier to operate, but the engine underneath is still a playbook engine. A human defines the workflow and the tool runs it, so coverage still tops out where the playbook library and the SIEM feed stop. A reasoning-first platform investigates each alert from its own context instead of matching it to a script, and it can only do that when it reasons over complete log data. The dividing line is architectural. A chat interface bolted onto the same engine does not move it.
Less than a SIEM migration, because the log data does not have to move. Search-in-place connectors read from S3, Splunk, Elastic, or an existing data lake where the logs already sit, so there is no rip-and-replace and no second storage bill. Most teams keep the SOAR playbooks that already work for well-defined alerts and route the unhandled queue, the majority that never fit a template, to agents that investigate without a script. The shift happens one detection surface at a time rather than in a single cutover.
The intelligence you need is already sitting in your logs. It has stayed out of reach because of a visibility problem that first-generation AI-over-SIEM platforms inherited and never fixed.