How Red Teaming Is Evolving In 2026 – And Why Your Business Can’t Afford To Test Yesterday’s Threats + Video

Introduction:

The rapid adoption of agentic AI, cloud-1ative architectures, and sprawling identity ecosystems has expanded the enterprise attack surface at an unprecedented rate—yet many organisations continue to test for threats they faced yesterday, not the AI-augmented adversaries of today. Traditional red teaming remains essential, but validating how well your teams detect, respond, and recover is becoming just as critical as identifying vulnerabilities. In 2026, effective red teaming has evolved from a point-in-time technical exercise into a strategic capability that strengthens security operations through attack chain-driven, collaborative engagements.

Learning Objectives:

Understand how AI and LLM adoption has created entirely new attack surfaces, including prompt injection, data leakage, and model manipulation vectors.
Master the convergence of red and purple teaming to translate offensive findings into measurable defensive improvements.
Learn to prioritise remediation through full attack chain simulation rather than isolated vulnerability testing.
Acquire practical Linux, Windows, and cloud CLI commands for AI security testing, detection validation, and infrastructure hardening.
Align red teaming strategies with regulatory frameworks like DORA’s Threat-Led Penetration Testing (TLPT) requirements.

You Should Know:

AI Has Created an Entirely New Attack Surface – Here’s How to Test It

The most obvious shift in the 2026 threat landscape is the growing use of agentic AI by attackers, enabling threat actors to operate at greater scale and speed while simultaneously lowering the barrier to entry for sophisticated attacks. However, the bigger challenge for most businesses isn’t attackers using AI—it’s the widespread, often poorly secured adoption of AI within their own environments. Organisations are rapidly integrating large language models (LLMs) into products, services, and internal workflows, yet security testing scoping and practices have not caught up. AI systems introduce prompt injection attacks, data leakage, model manipulation, excessive permissions, and deterministic behaviours that create opportunities for attackers to access sensitive information. Red teams are increasingly finding AI-enabled systems to be the path of least resistance during engagements.

Step-by-Step Guide: Testing AI Systems for Prompt Injection and Model Vulnerabilities

Step 1: Deploy an LLM vulnerability scanner. Use NVIDIA’s open-source Garak tool to systematically probe your LLM for security weaknesses:

 Install Garak
pip install garak

Run a comprehensive scan against your OpenAI model
python3 -m garak --model_type openai --model_name gpt-4 --probes all

Test specific prompt injection vectors
python3 -m garak --model_type openai --model_name gpt-4 --probes dan prompt_injection

Garak checks for hallucinations, prompt injections, jailbreaks, data leakage, and toxic outputs.

Step 2: Test for command injection via LLM interfaces. Use Promptfoo’s Shell Injection plugin to test whether prompts can push tool-using systems toward unauthorised command execution:

 Install Promptfoo
npm install -g promptfoo

Run shell injection test suite
promptfoo eval --config promptfoo.yaml --plugins shell-injection

Step 3: Implement AI-specific red teaming with the AIx framework. This AI penetration testing framework provides direct and indirect prompt injection, jailbreak variants (DAN, roleplay, developer mode), and uses a secondary LLM as a judge to evaluate attack success:

 Clone and run AIx
git clone https://github.com/licitrasimone/aix-framework
cd aix-framework
python3 aix.py --target your-llm-endpoint --attack prompt-injection

Step 4: Audit AI permissions and data exposure. Review excessive permissions granted to AI services and implement data loss prevention (DLP) controls around LLM inputs and outputs. Red teaming must now include AI systems as a distinct security domain rather than treating them as simple business tools.

Red and Purple Teaming Are Converging – Validate Detection, Not Just Vulnerabilities

Traditional red teaming has often been viewed as an adversarial exercise designed to demonstrate how an attacker could compromise a business. While valuable, this approach can leave security teams with a report of findings but limited understanding of how the attack unfolded or how to prevent it from happening again. Modern red team engagements frequently involve cutting-edge techniques and bespoke tooling that closely resemble zero-day attacks—simply presenting the outcome isn’t enough to deliver meaningful improvements. Businesses increasingly demand to understand what techniques were used, how the attack succeeded, which controls failed, how similar activity can be detected in future, and which defensive improvements will deliver the greatest impact.

Step-by-Step Guide: Implementing Purple Teaming for Detection Validation

Step 1: Deploy MITRE Caldera for adversary emulation. Caldera is an open-source platform that automates adversary emulation and is built on the MITRE ATT&CK framework:

 Clone and run Caldera
git clone https://github.com/mitre/caldera.git
cd caldera
python3 server.py --insecure

Access the web interface at https://localhost:8888
 Load the Atomic plugin to convert Atomic Red Team tests into Caldera abilities

Caldera is the default open-source choice for purple-team exercises, SIEM detection validation, and EDR coverage testing.

Step 2: Execute attack techniques and monitor detection in real-time. During a purple team session, the red team executes techniques while the blue team monitors for detection, and both collaborate to fix gaps immediately. For each technique, record whether it was detected (alert or analyst within SLA), missed (no timely detection), or blocked (control prevented the technique).

Step 3: Create or refine detection rules based on purple team findings. For every detection gap identified, collaborate to create or refine detection rules—Sigma rules for SIEM, KQL queries for Microsoft Sentinel, SPL for Splunk, or EDR custom detection policies:

 Example Sigma rule for detecting suspicious LSASS access
title: Suspicious LSASS Access
status: experimental
description: Detects suspicious access to LSASS process
logsource:
product: windows
service: security
detection:
selection:
EventID: 4656
ObjectName: '\lsass.exe'
AccessMask: '0x1010'
condition: selection

Step 4: Operate in a “mobius loop” pattern. Have blue teams generate threat intelligence that informs red team activity, creating a continuous feedback loop. This approach delivers a far greater return on investment from red team exercises, tangible improvements in defensive maturity, and the ability to align with regulatory standards like DORA.

Attack Chain Simulation – Prioritise What Matters Most

Security teams are facing an unprecedented volume of vulnerabilities, alerts, and remediation activities. Fixing everything simply isn’t realistic—understanding what matters most has become just as important as identifying weaknesses in the first place. Attack chain simulation focuses on how attackers operate in the real world, rather than demonstrating a single vulnerability or control weakness in isolation. Red teams simulate an end-to-end compromise to understand how multiple issues can be combined to achieve a specific objective.

Step-by-Step Guide: Building and Testing Attack Chains

Step 1: Use Atomic Red Team for granular ATT&CK simulation. Atomic Red Team provides a library of tests mapped to the MITRE ATT&CK Framework:

 On Windows, run Atomic Red Team tests via PowerShell
Import-Module -1ame "C:\AtomicRedTeam\invoke-atomicredteam.ps1" -Force
Invoke-AtomicTest T1059.001 -TestNumbers 1  PowerShell execution
Invoke-AtomicTest T1003.001 -TestNumbers 1  LSASS credential dumping

Step 2: Chain multiple TTPs into a complete attack path. Rather than testing individual exploits, simulate a full adversary campaign:

 Linux: Simulate initial access, privilege escalation, and lateral movement
 Initial access: Phishing or exploit
 Privilege escalation: Kernel exploit or sudo misconfiguration
sudo -l  Check sudo permissions
find / -perm -4000 -type f 2>/dev/null  Find SUID binaries

Lateral movement: SSH key harvesting
find /home -1ame ".ssh" -type d 2>/dev/null
cat ~/.ssh/authorized_keys

Step 3: Identify critical control failures. Attack chain simulation helps identify which control failures are genuinely critical, highlights where businesses should prioritise remediation efforts, and demonstrates how attackers move through environments once initial access is gained. In many cases, addressing one or two critical control failures can prevent multiple attack scenarios from succeeding.

Step 4: Focus on breaking attack paths early. At a time when vulnerability discovery continues to accelerate and security teams remain under significant pressure, prioritisation is essential. Red teaming should help businesses make better security decisions, not simply provide a longer list of issues to fix.

Cloud and Identity Security – The New Frontline

Businesses that have invested heavily in securing their infrastructure and applications may inadvertently expose critical data through poorly implemented cloud and identity configurations. Red teaming can no longer focus solely on networks, endpoints, and applications—security testing must evolve to include cloud-1ative services and identity ecosystems.

Step-by-Step Guide: Cloud and Identity Hardening

Step 1: Scan for cloud misconfigurations. Use Prowler, an open-source security tool for AWS, Azure, and GCP that performs security best practices assessments, audits, and continuous monitoring:

 AWS
prowler aws --regions us-east-1,us-west-2

Azure
prowler azure --subscription-ids <subscription_id>

GCP
prowler gcp --project-ids <project_id>

Prowler includes CIS, NIST 800, NIST CSF, CISA, FedRAMP, PCI-DSS, GDPR, HIPAA, and SOC2 benchmarks.

Step 2: Audit identity attack paths. The identity layer is often the last line of defence—organisations that think they have identity monitoring often have a log-aggregation theatre problem: they forward AD event logs to a SIEM, write a handful of rules for obvious anomalies, and call it covered. Implement per-principal behavioural baselines and monitor critical Active Directory events:

| Event ID | Location | Significance |

|-|-|–|

Step 3: Harden identity configurations. Run these commands to identify and mitigate common identity attack vectors:

 Windows: Check for privileged groups
net localgroup administrators
net group "Domain Admins" /domain

Audit Kerberos encryption types
klist

Check for SPN misconfigurations (Kerberoasting target)
setspn -T <domain> -Q /

Step 4: Implement continuous cloud and identity threat detection. Integrate cloud security posture management (CSPM) and identity threat detection and response (ITDR) into your security operations. Purple team exercises should specifically test cloud-1ative attack paths, including misconfigured storage, overly permissive IAM roles, and identity-based privilege escalation.

Aligning Red Teaming with Regulatory Frameworks – DORA and Beyond

The role of red teaming in 2026 is fundamentally different from what it was just a few years ago. Businesses can no longer view it as an occasional penetration test designed to tick a compliance box or validate a single control. Under the Digital Operational Resilience Act (DORA), significant financial institutions must conduct advanced operational resilience testing by means of Threat-Led Penetration Testing (TLPT) at least every three years. DORA’s TLPT requirements are explicitly aligned with the TIBER-EU framework, and purple team exercises are now mandatory.

Step-by-Step Guide: Preparing for DORA TLPT Compliance

Step 1: Understand TLPT requirements. DORA mandates realistic simulation of advanced persistent threats (APTs) tailored to the entity’s specific threat landscape. The active red-teaming phase must last at least 12 weeks to realistically emulate stealthy threat actors.

Step 2: Ensure testing team independence and competency. Testers must meet strict criteria regarding independence and qualifications. DORA requires an independent threat intelligence provider and a separate red team provider.

Step 3: Document threat-led approach. DORA asks for evidence of threat-led approaches. Maintain comprehensive documentation of:

Threat intelligence used to inform testing scenarios
Attack chains simulated and their MITRE ATT&CK mappings
Detection and response validation results
Remediation actions taken and their effectiveness

Step 4: Integrate purple teaming into compliance reporting. With purple team exercises now mandatory under DORA, ensure your purple team activities are documented as part of your operational resilience testing programme.

What Undercode Say:

Key Takeaway 1: The question isn’t whether your organisation has security controls in place—it’s whether those controls will perform when faced with modern, AI-augmented adversaries. Red teaming must evolve from point-in-time testing to continuous, collaborative attack chain simulation that validates detection and response capabilities alongside vulnerability identification.
Key Takeaway 2: The convergence of red and purple teaming, AI-specific attack surface testing, and attack chain prioritisation represents a fundamental shift in how businesses must approach offensive security. Organisations that treat AI as a new security domain, embrace collaborative purple teaming, and focus on breaking attack paths early will be significantly better positioned to defend against the threats of today and tomorrow.

Analysis: The cybersecurity industry is at an inflection point. The widespread adoption of AI by both defenders and attackers is creating an asymmetric threat landscape where traditional testing methodologies are no longer sufficient. Red teaming in 2026 must answer broader questions: Could an attacker exploit our AI systems? How would our defensive teams detect and respond to a sophisticated attack? Which control failures represent our greatest risk? The most valuable red team engagements are those that help businesses understand how attackers think, improve defensive capabilities, and prioritise investments that meaningfully reduce risk. As attack techniques continue to evolve and regulatory requirements like DORA mandate threat-led testing, red teaming is becoming a strategic business imperative rather than a technical checkbox.

Prediction:

+1 Organisations that embrace AI-specific red teaming and purple team convergence will develop a significant competitive advantage in cyber resilience, reducing mean time to detect (MTTD) and mean time to respond (MTTR) by 40–60% over the next 18–24 months.
+1 The market for AI security testing tools and purple teaming services will experience exponential growth, with Gartner predicting a 300% increase in AI red teaming engagements by 2028 as regulatory frameworks like DORA and the EU AI Act mandate rigorous testing.
-1 Businesses that fail to evolve their red teaming strategies beyond traditional penetration testing will face increasing regulatory fines, breach costs, and reputational damage as AI-enabled attackers systematically exploit poorly secured AI systems and identity infrastructure.
-1 The skills gap in AI security and purple teaming will widen significantly, creating a talent shortage that leaves many organisations unable to adequately test and defend against AI-augmented adversaries through 2027.
+1 The convergence of red and purple teaming will drive the development of continuous threat exposure management (CTEM) programmes that integrate offensive security, detection validation, and automated remediation into a single, measurable framework.

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