SentinelOne Static AI & Behavioral AI — Autonomous Detection Without Cloud Dependency

① The two AI engines — Static before, Behavioral during

SentinelOne's detection story starts with a key architectural decision: put the AI on the agent, not in the cloud. The Singularity agent bundles two trained models that make autonomous decisions locally, with no dependency on signatures, pattern files or cloud connectivity. Both models are updated through normal agent upgrades, not through daily definition downloads.

The two engines cover different moments in an attack. Static AI acts at the pre-execution gate — before any suspicious binary is allowed to launch, Static AI classifies it as benign, suspicious or malicious using a trained decision-tree model. Behavioral AI takes over once processes are running — it monitors every process, its children, the files it touches, the registry keys it modifies, the network sockets it opens, and crucially the relationships between processes, so it can spot ransomware encryption loops, lateral movement chains and fileless payloads that arrive purely in memory.

Together the two engines give SentinelOne layered, cloud-independent coverage: Static AI eliminates file-based threats before they start; Behavioral AI catches the threats that have no file to scan.

② Static AI — blocking threats before a single instruction runs

Static AI is a classifier engine that inspects a file at access time — before execution. The model has been trained on a very large corpus of benign and malicious samples accumulated over many years of production telemetry. Given a new binary, the engine extracts features (PE header structure, entropy, section names, import table, strings) and runs them through a decision-tree algorithm that returns a confidence score and a verdict: benign, suspicious or malicious. No signature, no cloud query, no internet connection required.

What Static AI catches and misses

Static AI excels at file-based malware: executables, DLLs, packed droppers, weaponised documents and scripts where malicious bytes are present on disk. It struggles with fileless threats — attacks that live entirely in memory, or that use legitimate OS tools like PowerShell or WMI with no malicious binary to classify. That gap is precisely where Behavioral AI picks up.

The decision-tree classifier also supports a sensitivity threshold: operators can tune aggressiveness (how low a confidence score triggers a block versus a quarantine versus a flag), letting security teams balance detection coverage against false-positive rate for their environment.

③ Behavioral AI — tracing every process during execution

Behavioral AI is vector-agnostic: it does not care whether the threat arrived as an EXE, a macro, a PowerShell script, a WMI subscription, or a reflective DLL injected straight into memory. What it cares about is what processes do over time. The engine traces every process on the endpoint and maps the full tree of parent-child relationships, file writes, registry modifications, network connections and inter-process interactions.

This approach makes Behavioral AI particularly effective against threats that Static AI cannot see: ransomware (spotted by the pattern of rapid, sequential file encryption), fileless malware (living-off-the-land binaries executing malicious payloads from memory), zero-days (novel exploits with no signature match), lateral movement (credential harvesting and pass-the-hash chains), and weaponised scripts (malicious PowerShell, VBScript or bash).

The storyline — events become context

Every event Behavioral AI observes is correlated into a storyline. Instead of thousands of raw alerts, a SOC analyst sees one incident with the full attack chain, enriched with MITRE ATT&CK technique tags. The Behavioral AI engine makes this judgment on the agent — no round-trip latency to the cloud — so it can shut down an attack chain at machine speed.

④ Autonomous response — kill, quarantine and rollback with no cloud required

When either AI engine returns a malicious verdict, the Singularity agent does not send an alert and wait for a human. It autonomously responds: it kills the offending process tree, quarantines the malicious file, and if ransomware has already encrypted some files before Behavioral AI detected the pattern, it can roll back the damage using a shadow-copy mechanism — restoring encrypted files to their pre-attack state. All of this happens on the agent, in real time, whether the endpoint has cloud connectivity or not.

This on-agent autonomy is the architectural claim SentinelOne calls ActiveEDR. The agent records every event into an immutable local story, syncs it to the Singularity platform when connected, and uses that storyline to drive both automated remediation and analyst investigation.

Deploy and tune

Operators can set each policy to Detect (alert only), Protect (alert + autonomous action), or Interoperability (reduced footprint for conflict-sensitive environments). Start in Detect mode on a new rollout, review the storyline queue, adjust the Static AI sensitivity threshold to cut false positives, then promote to Protect once confidence is high. The rollback feature makes Protect mode lower-risk than it sounds — even if a legitimate process is killed, the files can be restored.