AI-driven applications are reshaping how software is built, deployed, and operated. Models are trained continuously, inference services scale elastically, and data flows across clouds and clusters at unprecedented speed. In this environment, traditional security controls focused on static configurations and perimeter defenses are no longer sufficient.
Modern security must assume that applications are always running, always changing, and always exposed. Runtime application security sits at the center of this shift, providing real-time visibility, detection, and response as workloads execute – exactly where AI systems are most vulnerable.
Critical for AI Workloads
AI workloads introduce unique runtime risks. Model servers dynamically load artifacts, data pipelines execute arbitrary code paths, and orchestration layers create and destroy resources automatically. These behaviors are legitimate, but they also create ideal cover for malicious activity.
Configuration scans and pre-deployment checks cannot detect:
- Compromised workloads executing unexpected binaries
- Live data exfiltration from inference services
- Privilege escalation inside containers or virtual machines
- Abuse of AI services to perform unintended actions
Runtime security focuses on actual behavior, allowing teams to detect and respond to threats as they occur rather than after damage is done.
Visibility at the System and Kernel Level
At the foundation of runtime security is deep visibility into system behavior. By observing low-level events such as process execution, file access, network activity, and privilege changes—security platforms can establish a clear picture of how applications behave in production.
This approach is effective across:
- Containers and Kubernetes clusters
- Virtual machines
- Bare-metal Linux hosts
By evaluating runtime activity against defined behavioral expectations, security teams can detect deviations that signal compromise, misuse, or policy violations.
Beyond Infrastructure
Modern AI platforms generate security-relevant signals far beyond the operating system. Runtime security must correlate system-level behavior with higher-layer events to provide meaningful context.
This expanded visibility includes:
- Kubernetes control-plane and audit events
- Cloud API and infrastructure activity
- Identity and access behavior
- CI/CD pipeline and source control actions
By connecting runtime execution with orchestration and identity data, organizations gain end-to-end insight into how AI systems are being used and abused in real time.
Confidential Computing
As AI workloads increasingly handle sensitive data, encryption at rest and in transit is no longer enough. Confidential Computing introduces hardware-backed isolation that protects data while it is being processed.
Using trusted execution environments (TEEs), confidential workloads ensure that:
- Memory contents are encrypted and isolated
- Even cloud operators cannot access sensitive data
- AI models and datasets remain protected during execution
When combined with runtime security, confidential computing creates a powerful defense-in-depth model protecting both what runs and how it behaves.
From Storage to Execution
Encryption remains a foundational pillar of runtime security, especially for AI systems that process proprietary or regulated data. Beyond traditional disk and network encryption, modern environments emphasize:
- Fine-grained key management and access control
- Customer-controlled encryption keys
- Strong auditability of cryptographic operations
Runtime security complements encryption by ensuring that only authorized processes and identities can access decrypted data during execution, closing the gap between cryptographic protection and real-world behavior.
DAST and Runtime Security
Dynamic Application Security Testing (DAST) plays a critical role in identifying vulnerabilities by interacting with running applications from the outside. It helps uncover issues such as injection flaws, authentication weaknesses, and exposed endpoints.
However, DAST alone cannot see what happens inside the application once it is running. Runtime security fills this blind spot by:
- Detecting exploit attempts that succeed past the application layer
- Observing malicious post-exploitation behavior
- Validating whether discovered vulnerabilities are actually abused
Together, DAST and runtime security provide a continuous feedback loop between testing and production defense.
AI-Enhanced Detection and Intelligent Response
As AI workloads scale, the volume of runtime events grows exponentially. Manual rule tuning and static thresholds quickly become impractical. AI-assisted analysis enhances runtime security by:
- Learning normal behavior patterns across environments
- Identifying subtle anomalies at scale
- Reducing false positives through contextual understanding
- Prioritizing alerts based on risk and potential impact
In effect, AI helps secure AI—turning complexity into an advantage rather than a liability.
Real-Time Detection, Response, and Forensics
Speed is everything at runtime. Security systems must detect and respond while an attack is in progress, not after logs are reviewed.
Effective runtime security enables:
- Immediate alerting on suspicious behavior
- Automated containment actions such as process termination or isolation
- Rich forensic data collection for investigation and compliance
- Integration with centralized security operations workflows
For AI systems, where misuse can propagate rapidly, this real-time capability is essential.
Runtime Security Across Multi-Cloud AI Platforms
AI platforms are inherently multi-cloud and hybrid, spanning Kubernetes clusters, managed services, and compute resources across AWS, Azure, and Google Cloud. Runtime security must therefore be portable and consistent.
A unified approach allows organizations to:
- Apply the same behavioral policies across environments
- Detect threats regardless of underlying infrastructure
- Correlate runtime activity with cloud-native security signals
- Support hybrid, edge, and regulated AI deployments
Mapping Runtime Detection to MITRE ATT&CK Framework
We spent several weeks analyzing runtime protection tools. The objective of this analysis was to evaluate how runtime application security detection logic aligns with the MITRE ATT&CK framework, focusing specifically on host-based and workload-level visibility. The assessment measured both breadth of coverage – how many ATT&CK techniques and sub-techniques were addressed across attack phases and depth of coverage, defined by the density and concentration of detection logic within each phase.
Beyond quantitative metrics, a qualitative review examined the intent and practical usefulness of individual detection rules. This included evaluating whether runtime signals would realistically surface attacker behavior in production environments, rather than generating purely theoretical or noisy alerts.
The results showed that runtime detection provides broader ATT&CK coverage than commonly assumed, particularly in phases where attacker activity directly intersects with application execution. Detection was strongest in Execution, Persistence, and Privilege Escalation, reflecting the natural advantage of observing real-time process behavior and system-level actions. Coverage across techniques and sub-techniques was generally robust, with a healthy balance between scope and depth.
Detection related to Credential Access was effective primarily when credentials were exposed or handled locally by workloads, highlighting both the strength and limitation of host-based monitoring. Conversely, network-centric attack phases showed weaker coverage, underscoring the need to complement runtime security with network and cloud-layer controls.
While the detection logic demonstrated meaningful real-world potential, it was not sufficient as a standalone security solution. Runtime alerts tended to be low-level and highly granular, requiring additional correlation, enrichment, and integration with higher-level security platforms to be operationally effective.
Overall, the analysis indicates that runtime application security plays a critical but complementary role in AppSec programs. When integrated with cloud, network, and application-layer controls, runtime detection significantly enhances visibility into active attacker behavior – especially in modern, AI-driven, cloud-native environments.
Conclusion
As AI systems gain autonomy, trust becomes a foundational requirement. Runtime application security augmented by confidential computing, strong encryption, DAST, and AI-driven analysis forms the last and most critical layer of defense.
By securing applications while they run, organizations can innovate with AI confidently, knowing they have visibility, control, and protection exactly where it matters most.
In the age of AI, security is no longer a gate, it is a continuous, intelligent runtime capability. We continue to push the boundaries of latest research at Pervaziv AI and we look forward to incorporate some of this work into our products as we see fit.
