RSAC 2026: Tag in a Partner for the AI Security Showdown
This promotional article highlights Akamai's upcoming presence at RSAC 2026, focusing on the escalating arms race between AI-driven cyber threats and enterprise security. It emphasizes that adversaries are using AI to automate API attacks and exploit cloud misconfigurations, necessitating a shift away from legacy security toward robust Zero Trust frameworks and strategic partner ecosystems.
Key Takeaways
- Adversaries are increasingly leveraging AI to automate complex logic attacks against APIs and rapidly exploit cloud misconfigurations.
- The rapid adoption of autonomous AI agents by enterprises is fundamentally expanding the attack surface.
- Legacy security architectures that rely on human response times are insufficient against AI-driven threats.
- Organizations must adopt inherently adaptable Zero Trust frameworks to secure their AI implementations and infrastructure.
Affected Systems
- Public-facing APIs
- Cloud Infrastructure
- Internal Network Segments
- Enterprise AI Agents
Attack Chain
Adversaries utilize artificial intelligence to automate complex logic attacks targeting public-facing APIs. Once initial access or reconnaissance is achieved, these automated tools silently navigate internal network segments and rapidly identify and exploit cloud misconfigurations, outpacing traditional human-reliant security responses.
Detection Availability
- YARA Rules: No
- Sigma Rules: No
- Snort/Suricata Rules: No
- KQL Queries: No
- Splunk SPL Queries: No
- EQL Queries: No
- Other Detection Logic: No
No detection rules or queries are provided in this high-level marketing article.
Detection Engineering Assessment
EDR Visibility: None — The article is a high-level marketing piece and does not discuss endpoint-level technical details, processes, or malware execution. Network Visibility: Low — While API attacks and lateral movement are mentioned conceptually, no specific network signatures, traffic patterns, or protocols are detailed. Detection Difficulty: Hard — Detecting AI-automated logic attacks against APIs and cloud infrastructure requires advanced behavioral analytics and anomaly detection, as these attacks often blend with legitimate traffic and execute faster than traditional thresholds.
Required Log Sources
- API Gateway Logs
- Cloud Audit Logs
- Network Flow Logs
Hunting Hypotheses
| Hypothesis | Telemetry | ATT&CK Stage | FP Risk |
|---|---|---|---|
| Automated AI tools are conducting rapid, complex logic attacks against public-facing APIs to identify vulnerabilities or bypass authentication. | API Gateway Logs | Initial Access | High (May blend with aggressive legitimate API clients, automated scrapers, or partner integrations) |
Control Gaps
- Legacy security architectures relying on human response times
Key Behavioral Indicators
- Unusually rapid sequence of complex API calls
- Anomalous lateral movement patterns across internal network segments
Recommendations
Immediate Mitigation
- N/A
Infrastructure Hardening
- Implement a Zero Trust framework to anchor infrastructure security and limit lateral movement.
- Audit cloud environments for misconfigurations that could be rapidly exploited by automated tools.
- Deploy behavioral analytics and rate limiting on public-facing APIs to mitigate automated logic attacks.
User Protection
- N/A
Security Awareness
- Ensure core security practices and cyber resilience are integrated into every stage of enterprise AI implementation.
MITRE ATT&CK Mapping
- T1588.007 - Obtain Capabilities: Artificial Intelligence
- T1190 - Exploit Public-Facing Application
- T1021 - Remote Services
