AI security and cyber security management

Introduction: why security is no longer a "reaction" but a management

Attacks became fast, distributed, and automated. The human speed of analyzing logs and alerts no longer has time. The AI security loop turns a raw telemetry stream into manageable solutions: detects anomalies, links signals between environments (cloud/endpoints/identity/network), explains the causes and automatically responds - from node isolation to policy updates and SOC notification.

1) Data: AI cyber defence foundation

Identity and access: authentication, MFA, privilege changes, provisioning, login failures, behavioral fingerprints.

Endpoints (EDR/XDR): processes, startup trees, network/disk connections, injections, antivirus verdicts.

Network and perimeter: NetFlow/PCAP, DNS/HTTP, proxy, WAF/CDN, VPN/ZTNA telemetry.

Clouds and SaaS: management API calls, IAM roles, configurations (CSPM), serverless/containers (K8s audit), storages.

Code and supply chain: repositories, CI/CD logs, SCA/SAST/DAST results, artifact signatures.

Mail and collab tools: letters, attachments, links, reactions, chat events (with consents).

TFeed/Threat Intel: indicators of compromise, tactics/techniques (TTP matrix), campaigns.

Principles: single event bus, normalization and deduplication, strict schemes (OpenTelemetry/OTEL-like), PII minimization, hashing/tokenization.

2) Feechee: How to code 'suspicious'

UEBA characteristics: deviation from the "normal" for the user/host/service (time, geo, device, access graph).

Process chains: incompatible launch trees, "living off the land," sudden ransomware.

Network patterns: late movements (lateral), beacons, single domains, TSL anomalies, DNS tunneling.

Identity and rights: escalations, service accounts with interactive login, permissions "wider than normal."

Cloud/DevOps: open buckets, unsafe secrets, IaC drift, suspicious changes to manifestos.

Mail/social engineering: BEC patterns, reply chain, domain look-alikes, spear phishing.

Graph of connections: who communicates with whom/what, which artifacts are repeated in incidents, which nodes are "bridges."

3) Model security stack

Rules and signatures: deterministic prohibitions, regulatory policies, IOC matches - first line.

Unsupervised anomalies: isolation forest, autoencoder, One-Class SVM over UEBA/network/clouds - to catch the "unknown."

Supervised scoring: boosts/logs/trees for prioritizing alerts and BEC/ATO cases (the main target is PR-AUC, precision @ k).

Sequences: RNN/Transformer for lateral patterns (lateral movement, C2-beacons, kill chain).

Graph analytics: communities of nodes/accounting/processes, centrality, link prediction - for supply chains and hidden connections.

Generative Assist: GPT hints for enriching alerts/timelines (only as a "copylot," not as a "solver").

XAI: SHAP/surrogate rules → explainable reasons with "what/where/why/what to do."

4) Orchestration & Response: SOAR "zel ./yellow ./red."

Green (low risk/false positive): auto-closing with a log of causes, training filters.

Yellow (doubt): automatic enrichment (VirusTotal-like, TI-feeds), file/attachment quarantine, MFA-challenge, ticket in SOC.

Red (high risk/verified): node/session isolation, forced reset password, token revocation, block in WAF/IDS, secret rotation, CSIRT/compliance notification, ransomware/BEC/ATO playbook launch.

All actions and inputs are placed in the audit trail (input → feature → scoring → policy → action).

5) Zero Trust with AI: Identity is the new perimeter

Contextual access: the risky speed of the user/device is mixed into the ZTNA solutions: somewhere we let it in, somewhere we ask for MFA, somewhere we block it.

Policies-as-code: describe access to data/secrets/internal services declaratively; validated in CI/CD.

Microsegmentation-Automatically suggests network policies based on communication graphs.

6) Clouds and containers: "security as configuration"

CSPM/CIEM: models find config drift, "redundant" IAM roles, public resources.

Kubernetes/Serverless: abnormal privileges, suspicious sidecars, unsigned images, jumps in network activity in the hearths.

Supply Chain: SBOM control, signing artifacts, tracking dependency vulnerabilities, alerting when a vulnerable path enters the prod.

7) E-mail and social engineering: WEIGHT/phishing/ATO

NLP radar: tonality, abnormal templates of requests for payment/details, domain substitution/display name.

Context verification: reconciliation with CRM/ERP (whether counterparty/amount/currency is allowed), chain confidence rate.

Auto-actions: "hold" the correspondence, request out-of-band confirmation, mark similar letters, revoke the link.

8) Ransomware and lateral movement incidents

Early signs: massive rename/encryption, CPU/IO jump, neighbor scan, suspicious AD accounts.

Answer: segment isolation, disabling SMB/WinRM, rolling back snapshots, revving keys, notifying IR commands, preparing a "golden image" for recovery.

XAI-timeline: a clear story "primary access → escalation → lateral movement → encryption."

9) Maturity and quality metrics

TTD/MTTD: detection time; MTTR: response time; TTK: time to "kill" the chain.

Precision/Recall/PR-AUC on marked incidents; FPR on green profiles (false alarms).

Attack Path Coverage - The proportion of TTPs covered by the scripting library.

Patch/Config Hygiene: average time to close critical vulnerabilities/drift.

User Trust/NPS: trust in actions (especially locks and MFA challenges).

Cost to Defend: reduced SOC hours per incident due to auto-enrichment/playbooks.

10) AI cyber defense architecture

Ingest & Normalize (log collectors, agents, API) → Data Lake + Feature Store (online/offline) → Detection Layer (rules + ML + sequences + graph) → XDR/UEBA → SOAR Decision Engine (zel ./yellow /red) → Action Fabric (EDR/WAF/IAM/K8s/Email/Proxy) → Audit & XAI → Dashboards & Reports

In parallel: Threat Intel Hub, Compliance Hub (policies/reports), Observability (metrics/tracks), Secret/SBOM Service.

11) Privacy, ethics and compliance

Data Minimization: collect as much as you need for the goal; strong pseudonymization.

Transparency: documentation of features/models/thresholds, version control, reproducibility of solutions.

Fairness: no systematic bias on geo/devices/roles; regular bias audits.

Jurisdictions: feature flags and various reporting formats for regions; data storage in the region.

12) MLOps/DevSecOps: the discipline without which AI "crumbles"

Versioning of datasets/features/models/thresholds and their lineage.

Drift monitoring of distributions and calibration; Shadow runs fast rollback.

Infrastructure tests: chaos-engineering logs/losses/delays.

Policies-as-code in CI/CD, stop gates on critical security regressions.

Sandboxes for synthetic attacks and red teams.

13) Implementation Roadmap (90 days → MVP; 6-9 months → maturity)

Weeks 1-4: single ingest, normalization, basic rules and UEBA v1, SOAR playbooks for top 5 scenarios, XAI explanations.

Weeks 5-8: graph-circuit (nodes: accounts/hosts/processes/services), sequence-detectors lateral movement, integration with IAM/EDR/WAF.

Weeks 9-12: XDR stitching oblako↔endpoynty↔set, BEC/ATO models, auto-isolation, compliance reports.

6-9 months: CSPM/CIEM, SBOM/Supply-chain, auto-calibration of thresholds, red timings and post-mortems according to XAI timelines.

14) Typical mistakes and how to avoid them

Expect "magic" from LLM. Generative models are assistants, not detectors. Put them behind XDR/UEBA, not before.

Blind sensitivity of models. Without calibration and guard metrics, you will drown in noise.

No count. Individual signals skip chains and campaigns.

Mix security and UX without XAI. Blocking without explanation undermines trust.

No DevSecOps. Without policy-as-code and rollback, any edit breaks production.

Collect "everything." Excess data = risk and expense; choose minimal-enough.

15) Before/after cases

BEC attempt: NLP notes an abnormal payment request, the graph associates the imitator domain with a well-known campaign → SOAR puts the correspondence on hold, requires an out-of-band confirmation, blocks the domain in the mail gateway.

Ransomware-early detection: surge rename + non-standard processes + beacon → segment isolation, SMB disable, snapshot rollback, IR notification, XAI report on attack steps.

ATO by identity: device change + geo, strange tokens → forced logout of all sessions, MFA-reset, analysis of recent actions, notification of the owner.

Cloud drift: the emergence of a redundant IAM role → auto-PR with the Terraform patch, alert to the service owner, check through policy-as-code.

AI security management is not a product, but a system: data discipline, explainable models, automated playbooks, and Zero Trust principles. Those who can combine detection speed, accuracy and calibration, decision transparency and operational readiness win. Then cyber defense from a reactive function turns into a predictable, verifiable skill of the organization.