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Introduction:
As AI-driven threats accelerate from months to milliseconds, technical debt and shadow IT have transformed from operational annoyances into critical vulnerabilities. JPMorgan Chase’s latest enterprise blueprint—released one week after joining Anthropic’s Project Glasswing—forces every CISO to confront a harsh reality: the “basics” of asset inventory, continuous scanning, and outbound filtering are no longer optional, and AI development must be secured with the same rigor as human-coded systems.
Learning Objectives:
- Implement continuous, AI-integrated vulnerability scanning across all pipelines and legacy systems.
- Harden enterprise perimeters by aggressively filtering outbound traffic and removing standing machine privileges.
- Embed security guardrails, red-teaming, and validation into the AI development lifecycle.
You Should Know
1. Complete Asset Inventory & Shadow Technology Discovery
Most breaches exploit assets the security team doesn’t know exist—developer-provisioned VMs, forgotten APIs, and shadow AI agents. JP Morgan demands a living inventory that includes every system, container, and SaaS dependency.
Step‑by‑step guide to discover shadow IT:
- Linux: Scan local network for unexpected hosts and open ports.
`sudo nmap -sn 192.168.1.0/24` (discover live hosts)
`sudo lsof -i -P -n | grep LISTEN` (list all listening services)
– Windows (PowerShell): Find all domain-joined and non-domain assets.
`Get-ADComputer -Filter | Select-Object Name, Enabled, LastLogonDate`
`Get-NetTCPConnection -State Listen`
- Cloud & containers: Use cloud provider APIs or open-source tools like `Trivy` to scan container images and running pods.
`trivy image –severity HIGH,CRITICAL your-app:latest`
- Automation script (bash): Combine
nmap,curl, and `jq` to feed unknown assets into a CMDB.nmap -sn 192.168.1.0/24 | grep 'Nmap scan' | awk '{print $5}' >> asset_list.txt while read ip; do curl -X POST https://your-cmdb/api/assets -d "{\"ip\":\"$ip\"}"; done < asset_list.txt
- Continuous Vulnerability Scanning – Not Quarterly, Not Weekly
Periodic scans are a liability when AI can weaponize a zero‑day in hours. Integrate scanning into every CI/CD pipeline and use AI models to auto‑fix issues.
Step‑by‑step integration:
- Install and run Trivy in CI (GitHub Actions example):
</li> <li>name: Run Trivy vulnerability scanner run: trivy fs --severity HIGH,CRITICAL --exit-code 1 --ignore-unfixed .
- Continuous network scanning: Use `nmap` with a cron job or `Zeek` for passive monitoring.
`sudo nmap -sV –script=vuln 192.168.1.0/24 -oA vuln_scan_$(date +%Y%m%d)`
- AI‑assisted remediation: Feed scan results to an LLM (e.g., via OpenAI API) to generate patch commands.
import openai prompt = f"Generate a Linux command to fix {vulnerability_cve}" response = openai.ChatCompletion.create(model="gpt-4", messages=[{"role":"user","content":prompt}]) - Windows: Use `Invoke-WebRequest` to query Microsoft Update Catalog API and `Get-WUInstall` for automated patching.
3. Aggressively Filter Outbound Traffic
Most production systems have no legitimate need to reach the open internet. Blocking egress would have stopped Log4Shell callbacks and SolarWinds beaconing.
Step‑by‑step egress hardening:
- Linux iptables default deny outbound (allow only essential):
sudo iptables -P OUTPUT DROP sudo iptables -A OUTPUT -m state --state ESTABLISHED,RELATED -j ACCEPT sudo iptables -A OUTPUT -p tcp --dport 443 -d your-package-repo.com -j ACCEPT sudo iptables -A OUTPUT -p udp --dport 53 -j ACCEPT
- Windows Firewall via PowerShell:
`New-NetFirewallRule -DisplayName “Block All Outbound” -Direction Outbound -Action Block`
then add allow rules for specific IPs/ports.
- Kubernetes network policies: Deny egress by default, allow only to necessary services.
apiVersion: networking.k8s.io/v1 kind: NetworkPolicy spec: policyTypes: [bash] egress:</li> <li>to: [namespaceSelector: {matchLabels: {name: "kube-system"}}] - Test egress filtering: Use `curl -I https://google.com` – it should fail unless explicitly allowed.
4. Remove Standing Privileges – Especially Machine Identities
Service accounts and API keys are often over‑privileged and rarely rotated, making them prime targets. JP Morgan insists on just‑in-time (JIT) access and identity‑aware authentication.
Step‑by‑step privilege removal:
- Linux: Remove unnecessary sudo rights and implement JIT with `sudo` timestamp_timeout.
`sudo visudo` – set `Defaults timestamp_timeout=0` to force re‑authentication.
Use `vault` for dynamic secrets:
`vault write database/creds/my-role ttl=1h`
- Windows: Convert service accounts to gMSA (Group Managed Service Accounts).
`New-ADServiceAccount -Name svc_app -DNSHostName app.domain.com -PrincipalsAllowedToRetrieveManagedPassword “SRV_AppPool”`
- Audit existing machine identities:
Linux: `cat /etc/passwd | grep -E “/(bin|sbin)” | awk -F: ‘{print $1}’`
Windows: `Get-WmiObject -Class Win32_Service | Where-Object {$_.StartName -like “$”} | Select Name, StartName` - Rotate secrets automatically: Use Azure Key Vault or AWS Secrets Manager with rotation lambdas.
5. Embed Security into Your AI Development Lifecycle
AI‑generated code can propagate vulnerabilities at machine speed. You must red‑team for prompt injection, data poisoning, and adversarial manipulation, then validate AI outputs with the same rigor as human code.
Step‑by‑step AI security guardrails:
- Prompt injection testing: Use `garak` (Generative AI Red‑teaming and Assessment Kit).
`garak –model_type huggingface –model_name meta-llama/Llama-2-7b –probes injection`
- Validate AI‑generated code (Python example with Bandit and LLM review):
import subprocess, openai ai_code = openai.ChatCompletion.create(model="gpt-4", messages=[{"role":"user","content":"Write a Python function to fetch URL"}]) with open("ai_generated.py", "w") as f: f.write(ai_code.choices[bash].message.content) result = subprocess.run(["bandit", "-r", "ai_generated.py"], capture_output=True) if result.returncode != 0: print("Security flaws found – rejecting AI code") - Continuous monitoring of AI assets: Log all LLM inputs/outputs to detect data leakage.
`curl -X POST https://your-ai-gateway/log -d ‘{“prompt”:”user query”,”response”:”model output”,”timestamp”:now}’` - Red‑teaming for adversarial manipulation: Use frameworks like `Counterfit` or `Adversarial Robustness Toolbox` (ART) to test model robustness.
git clone https://github.com/Azure/Counterfit.git cd Counterfit && python counterfit.py --target your-ai-endpoint --attack prompt_injection
- Guardrail rules for AI agents: Implement an allowlist of system commands the AI can execute.
{"allowed_actions": ["read_logs", "suggest_patch"], "denied": ["exec", "curl_http"]}
- Optimize Patching Speed – Mean‑Time‑to‑Patch as Board Metric
Every day between patch availability and deployment is unnecessary exposure. Automate where possible, but always test.
Step‑by‑step patching acceleration:
- Linux (Debian/Ubuntu): Use `unattended-upgrades` for security patches only.
`sudo apt update && sudo unattended-upgrades -d`
To simulate and report: `sudo apt upgrade –dry-run | grep -i security`
– Windows: Use `PSWindowsUpdate` module for scheduled patching.
`Install-Module PSWindowsUpdate`
`Get-WUInstall -AcceptAll -AutoReboot -Category “Security Updates”`
- Automation with Ansible: Patch all servers in parallel.
</li> <li>name: Apply security updates hosts: all tasks:</li> <li>name: Update apt cache apt: update_cache=yes cache_valid_time=3600</li> <li>name: Install security-only upgrades apt: upgrade=dist only_security=yes
- Measure MTTP: Log patch deployment timestamps.
`echo “$(date) – Patch KB123456 deployed to $(hostname)” >> /var/log/patching_metrics.log`
7. Segment Everything – Authenticate Every Connection
One compromised system should not take down the enterprise. Micro‑segmentation, VLANs, and zero‑trust network access (ZTNA) are mandatory.
Step‑by‑step segmentation:
- Linux iptables isolation (separate web from DB):
Allow web server to DB on port 3306 only sudo iptables -A FORWARD -s 192.168.1.10 -d 192.168.2.10 -p tcp --dport 3306 -j ACCEPT sudo iptables -A FORWARD -j DROP
- Windows Firewall advanced security: Create rules that restrict traffic by user, computer, or application.
`New-NetFirewallRule -DisplayName “Block RDP from non-IT subnet” -Direction Inbound -Protocol TCP -LocalPort 3389 -RemoteAddress 192.168.100.0/24 -Action Block` - Docker network segmentation:
docker network create --internal backend docker network create --driver bridge frontend docker run --network frontend --name web nginx docker run --network backend --name db mysql
- Kubernetes network policy to deny cross‑namespace traffic by default:
apiVersion: networking.k8s.io/v1 kind: NetworkPolicy metadata: {name: deny-cross-namespace} spec: podSelector: {} policyTypes: [bash] ingress: []
What Undercode Say
- Legacy hygiene is now critical infrastructure. Ignoring technical debt or shadow IT is equivalent to leaving a backdoor open for AI‑powered worms.
- AI security cannot be an afterthought. Prompt injection and adversarial manipulation are not theoretical; red‑teaming must become part of every AI deployment pipeline, with guardrails enforced at the API gateway.
The JPMorgan list validates what many practitioners have argued for years: the fundamentals (asset inventory, continuous scanning, egress filtering, privilege removal) remain the highest‑ROI controls. However, the new twist is velocity—AI compresses attack and defense timelines from weeks to seconds. Enterprises that fail to automate patching, scanning, and AI validation will be overwhelmed. The Mythos incident and OpenAI TAC revelations (referenced in the post) underscore that agentic AI systems are already being exploited. The bank’s call to “embed security into the AI development lifecycle” is not marketing—it’s survival.
Prediction
By Q1 2027, regulatory bodies (e.g., NYDFS, SEC) will mandate real‑time asset inventory and continuous vulnerability scanning for financial institutions, with mean‑time‑to‑patch becoming a audited metric. Simultaneously, AI‑specific attacks—particularly prompt injection against autonomous agents—will cause at least two major public breaches, forcing the adoption of mandatory red‑teaming certifications for any LLM used in production. Organizations that delay implementing JP Morgan’s 10 actions will face not only security incidents but also regulatory fines and shareholder lawsuits. The divide between “AI‑ready” and “legacy‑stuck” enterprises will become the new digital chasm.
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