Type "crowdstrike alternative" into a search bar and you are usually hunting for the wrong thing. CrowdStrike answers one question well: is this host compromised. It watches processes, network connections, and file activity, and for the threats it was built to catch, it earns its place on the box. Keep it.
The gap it leaves is a different question entirely. Is your most valuable data leaving on access you granted on purpose, in a pattern that does not fit. A finance dataset gets read in one account. Minutes later, a copy of it leaves from another. Every step holds a valid credential and a permitted action, so CrowdStrike sees a clean host the whole way through. Every tool that checks permissions agrees. The breach is the pattern across the moves, and nothing in the endpoint stack is built to see it.
Where this sits in the stack
Defense in depth means each layer watches a different thing. Stack them and you get a perimeter tool baselining the network, an email gateway scoring inbound, EDR baselining the host and the process. They overlap less than buyers fear and leave one seam wide open.
None of those tools is built to answer the data movement question: which dataset just left, to where, under whose identity, and does that movement fit how legitimate work actually moves. EDR knows the process was allowed to run. The perimeter knows the connection was allowed to open. Neither knows the connection carried a quarter of your customer table out under a developer who has never touched it.
That seam is where Hilt sits. It does not take CrowdStrike's slot. It takes the slot nothing in your stack occupies today: a runtime view of where valuable data goes, resolved to the job behind each move, surfaced while the data is still moving.
What it watches, and what it refuses to read
EDR asks whether a host or process is behaving like malware. Hilt asks whether the data is moving the way legitimate work moves. Different question, different vantage.
One lightweight collector runs single-tenant inside your own cloud (AWS, GCP, Azure, Ali Cloud) or on macOS-compatible user endpoints. It watches data movement at the kernel, the one place every move is visible no matter which application started it. By default it reads metadata only: which dataset moved, how much, where to, under which identity and job. It never has to open your files to do its work, and the events never leave your account. Content inspection is there if a team wants it. It is never the default and never required.
The collector sits off the path. It does not go inline. It does not block, drop, or alter traffic. The footprint runs around 0.1% of one core and 4 to 8 MB of memory, small enough to vanish into the noise floor next to the agents already fighting for the box. You add a layer of sight without adding a layer of latency.
What comes out is not another firehose of process alerts. Hilt resolves each move to an identity and the job behind it, then surfaces the move that does not fit: a sensitive dataset leaving under an identity that has never touched it, at a volume and to a destination legitimate work never produces. Where EDR sees a permitted connection from a permitted process, Hilt sees what that connection carried and weighs it against how that identity normally moves data.
The footprint, stated plainly
Watching at the kernel earned a reputation for overhead because older approaches wedged themselves into the path of execution. Hilt does not. The collector observes off the path, so it adds no checkpoint and changes no traffic.
In practice that means around 0.1% of one core and 4 to 8 MB of memory under normal load. It coexists with EDR, perimeter agents, and everything else already on the host. Metadata by default, off the path, single-tenant in your own cloud: those three choices are what keep it cheap. The data movement layer runs everywhere your valuable data lives, and you never have to trade seeing the movement for keeping the system fast.
Why one axis of baselining misses it
Point-in-time detection finds known bad. It does not find the move that does not fit, because that move is built entirely out of permitted parts.
Most tools baseline on one axis: per-host, per-process, or per-network segment. Hilt baselines the movement itself across the things that make a move legitimate or not, the identity behind it, the role that identity plays, and the environment the data moves within.
Picture two exports. An SRE pulls a dataset from a production store as part of a scheduled job. Normal. The same volume of the same data leaves under a developer identity that has never read it, to a destination legitimate work never uses. Not normal. A bulk export during a known migration window is fine. The same export with no job behind it is the one worth surfacing. Single-axis baselining cannot separate these, because each underlying action is individually permitted. Resolving the move to an identity and a job, and judging it against how that identity actually moves data, can.
The identity Hilt resolves is probabilistic and depends on what the source environment exposes. The system states how confident it is rather than pretending every move arrives labeled. And it does not wait for forensics to do this. The pattern surfaces while the data is still moving, in time to act.
When a move warrants it, the response is host-level network isolation (quarantine), issued from the control plane. The collector never sits inline and never blocks traffic itself. Isolation happens at the network, on the host in question, so a confirmed anomaly is contained without a chokepoint in front of legitimate data movement.
The complement, not the swap
"Alternative" implies you rip one tool out and drop another in. That is rarely how enterprise security gets built. You keep CrowdStrike for endpoint detection, threat intelligence, and response orchestration. You add the layer that answers what EDR was never built to answer: is the valuable data leaving in a pattern that does not fit.
You already cover the host and the network well. The open question is whether anything you run watches the data itself once access has been granted. Valuable data moves across cloud workloads, SaaS, user endpoints, and AI agents, all on access you granted on purpose. The gap is not at the endpoint. It is in the movement, where every action is permitted and only the pattern shows the breach.
That is an engineering call, not a slogan: can your architecture see data movement at runtime, resolved to identity, in time to act. Most cannot. If you want to walk through how the collector would sit in your stack, book a 30-minute technical call and we will trace a real move end to end.