Abnormal AI vs Sweet Security

About Sweet Security

Sweet Security agents learn how each application, agent and workload should behave. Sweet Security blocks a rogue agent the moment it acts and traces the full attack path from prompt to data.^[2]

Sweet Security combines Cloud Detection and Response (CDR), Application Detection and Response (ADR) and Cloud Workload Protection (CWPP) to detect attacks across modern cloud environments. Sweet Security unifies data from eBPF sensors and cloud logs to create a behavioral baseline of each environment. Sweet Security uses an eBPF-based sensor that performs application profiling. Sweet Security scores incidents and states what requires immediate attention. Sweet Security lets teams terminate malicious processes, manually or automatically, without disrupting the production environment.^[2][3][4]

Sweet Security gives SOC and Incident Response teams a complete picture of incidents across the cloud, workload, and application layers. Sweet Security captures processes, file accesses, network traffic and memory usage. Sweet Security tags events using the MITRE ATT&CK framework. Sweet Security correlates signals from every layer into a single incident. Sweet Security sends alerts via Slack, Jira, ServiceNow or custom webhooks. Sweet Security integrates with SOAR tools like Torq to automate full incident containment. Sweet Security lets teams build custom detections using signals from cloud logs, workload data, or L7.^[4]

Sweet Security shows runtime communication of cloud assets, both internally and externally. Sweet Security keeps an up-to-date overview of clusters, networks, databases, secrets and operating systems. Sweet Security identifies connections to external entities, unauthorized ports and connections to malicious DNSs or IPs. Sweet Security discovers human and non-human identities such as service accounts, IAM users, API keys and tokens. Sweet Security detects identity-based anomalies such as unauthorized access attempts, unusual privilege escalations and irregular usage patterns. Sweet Security detects secrets stored in plaintext or exposed in environment variables. Sweet Security integrates with identity providers (OIDC).^[5][6][9]

Sweet Security evaluates the risk and business impact of each vulnerability by combining LLMs with real-time runtime context. Sweet Security prioritizes vulnerabilities based on real-world exploitability indicators. Sweet Security builds a runtime-powered SBOM. Sweet Security detects misconfigurations in real time, not just through periodic scans. Sweet Security runs ongoing compliance checks across frameworks including HIPAA, CIS Kubernetes Benchmark, GDPR, NIST and SOC2. Sweet Security gives a unified view of toxic combinations by cross-correlating misconfigurations, exposed assets, executed vulnerabilities and over-permissive access.^[7][8]

Sweet Security uses Layer 7 visibility to understand API behavior, detect malicious activity and stop application-layer threats before they escalate. Sweet Security organizes APIs by individual endpoints to track request methods, error rates and potential vulnerabilities. Sweet Security identifies misconfigurations, exposed APIs and outdated dependencies across an API ecosystem. Sweet Security monitors first- and third-party application behavior at runtime to detect anomalies, unauthorized activity and signs of active exploitation. Sweet Security analyzes application behavior at the package and function level. Sweet Security can automatically block malicious activity in real time. Sweet Security filters attack attempts by threat type, such as SQLi, XSS and SSRF, and by status code.^[9][10]

Sweet Security continuously monitors AI infrastructure, including agents, models and APIs, to detect and neutralize risks as they emerge. Sweet Security automatically discovers AI services through API (L7) analysis and builds a real-time inventory of AI agents and their data flows. Sweet Security identifies unsanctioned AI tools and models operating outside IT and security oversight, such as rogue APIs or unmanaged ML models. Sweet Security detects sensitive data in prompts, unauthenticated or unencrypted AI access, and other policy violations. Sweet Security monitors for Personally Identifiable Information (PII), secrets and other sensitive data traversing AI workflows. Sweet Security identifies hidden or unauthorized APIs that may expose sensitive data.^[11]

Sweet Security gives AppSec teams continuous visibility into how applications behave, including what APIs are exposed, which vulnerabilities are exploitable, where sensitive data flows and where attackers are actively probing. Sweet Security unites security and development teams around shared runtime context. Sweet Security offers SOC, IR, DevSecOps and AppSec teams integrations across SIEMs, SOARs, alerting and ticketing systems.^{[3][7][8][10][12][13]}