The MarSec Schema

The ASTE Scorecard: A Self‑Assessment for the Eight Disciplines

You cannot manage what you do not measure. The ASTE maturity model (Week 3) gives you a stage. The scorecard gives you granular insight into each of the eight disciplines. Where are you strong? Where are you vulnerable? What should you fix first? This scorecard is designed for self assessment. Gather your marketing, security, and data leaders. Spend two hours. Be honest. The only person you cheat by over scoring is yourself.

Latest Posts

The Trust Auditor: Training Non‑Technical Teams to Protect Narrative Integrity

You have a narrative ledger. You have structured data. You have monitoring tools.
But the person updating your LinkedIn company page is an intern. The person responding to G2 reviews is a customer support agent. The person writing your podcast descriptions is a content coordinator.
If these team members do not understand narrative integrity, your infrastructure is useless.
The strongest cybersecurity strategy does not start with a firewall. It starts with humans: aware, aligned, resilient. The same is true for narrative security.
You need to train every person who touches your digital footprint to be a trust auditor.

May 26, 2026 No Comments

The Distributed Content Architecture: Managing Fragments Across Your Entire Digital Footprint

Your brand is not a single narrative. It is thousands of fragments distributed across dozens of platforms, each with its own structure, each with its own retrieval logic.
A podcast episode mentions your product. A Reddit comment describes your service. A review site user posts a photo of your packaging. A partner’s LinkedIn article quotes your CEO. A forum thread links to your documentation.
Each fragment is a data point for AI retrieval systems. Each fragment can be accurate or distorted. Each fragment contributes to your trust density or detracts from it.
You cannot control every fragment. But you can architect a system that makes accurate fragments more likely and distorted fragments less damaging.
This is distributed content architecture.

May 26, 2026 No Comments

Optimizing for Social AI: How Recommendation Engines Discover Your Brand

Social media algorithms are AI agents.
They read your content before humans do. They extract entities. They categorize your brand. They decide whether to surface your posts to followers or suppress them.
But unlike LLM based assistants, social AI agents have a different objective: maximize engagement and time on platform. They are not trying to answer questions accurately. They are trying to predict what content will keep users scrolling.
This changes how you optimize.
Optimizing for Google’s search AI is about verifiability and relevance. Optimizing for LinkedIn’s feed AI is about engagement prediction and entity coherence.
You need both.

May 26, 2026 No Comments

How the Scorecard Works

Each of the eight disciplines has five criteria. Score each criterion 0‑4:

0 = Not in place / we do not do this
1 = Partial / inconsistent / ad hoc
2 = Implemented but basic / needs improvement
3 = Good / consistently applied
4 = Excellent / best practice / automated

Sum the five criteria for a discipline score (0‑20). Then sum all eight for a total score (0‑160).

Discipline One: Cybersecurity Architecture (Narrative Focus)

Criterion	Score (0-4)
1.1 Narrative assets (brand claims, ledgers) are inventoried and classified by sensitivity
1.2 Access controls exist for who can modify the narrative ledger
1.3 Integrity checks (e.g., version control, hash validation) are applied to the narrative ledger
1.4 Third‑party access to narrative assets (agencies, partners) is reviewed quarterly
1.5 There is a process to revoke narrative access when employees or partners leave

Discipline One Total (max 20): _____

Discipline Two: Data Architecture (Semantic Focus)

Criterion	Score (0-4)
2.1 A knowledge graph or entity‑relationship model exists for core brand entities
2.2 Entity naming is canonical and enforced across owned channels
2.3 Structured data (schema.org, Open Graph, Twitter Cards) is implemented on key pages
2.4 There is a process to update the knowledge graph when positioning changes
2.5 External ontologies (Wikidata, DBpedia, industry taxonomies) are mapped where relevant

Discipline Two Total (max 20): _____

Discipline Three: Prompt Engineering & AI Literacy

Criterion	Score (0-4)
3.1 Marketing and content teams have been trained on how LLMs retrieve information
3.2 At least one person can write effective prompts for retrieval auditing
3.3 The team understands entity salience and semantic density
3.4 There is a library of test prompts used for weekly retrieval audits
3.5 Results from prompt audits are used to improve content and structure

Discipline Three Total (max 20): _____

Discipline Four: Content Strategy (Machine‑Readable First)

Criterion	Score (0-4)
4.1 Content is written with entity consistency (canonical names used throughout)
4.2 Claims are specific and verifiable (Level 2+ on verifiability hierarchy)
4.3 Each piece of content includes structured metadata (not just keywords)
4.4 Content is repurposed across platforms without losing semantic integrity
4.5 There is a quarterly content audit for drift and misalignment

Discipline Four Total (max 20): _____

Discipline Five: Brand Architecture (Trust‑Based)

Criterion	Score (0-4)
5.1 Brand positioning is documented and aligned with the narrative ledger
5.2 Brand guidelines include entity naming rules and claim consistency standards
5.3 All brand touchpoints (website, social, print, events) are audited for consistency
5.4 There is a process to update brand guidelines when the narrative evolves
5.5 Trust density is tracked as a brand health metric

Discipline Five Total (max 20): _____

Discipline Six: Crisis Management (Narrative Incident Response)

Criterion	Score (0-4)
6.1 A narrative incident response plan exists (playbooks for drift, hallucination, misalignment)
6.2 Roles are assigned (incident commander, comms lead, technical lead)
6.3 Tabletop exercises are run at least quarterly
6.4 There is a process to submit corrections to LLM providers and platforms
6.5 Post‑incident reviews are conducted and improvements implemented

Discipline Six Total (max 20): _____

Discipline Seven: Capital Readiness (Verifiable Trust)

Criterion	Score (0-4)
7.1 Investor‑facing claims are verified (Level 2 or 3)
7.2 A due diligence narrative ledger exists for investors
7.3 Trust density metrics are included in investor updates
7.4 There is a process to correct investor‑facing hallucinations
7.5 Reference customers have been trained on consistent narrative

Discipline Seven Total (max 20): _____

Discipline Eight: Community Building (Human OS)

Criterion	Score (0-4)
8.1 Employees are trained on narrative consistency and entity naming
8.2 There is a process for employee social media to align with brand narrative
8.3 Community managers understand entity extraction and drift
8.4 Feedback from communities is used to update the narrative ledger
8.5 Mission alignment is measured and tracked

Discipline Eight Total (max 20): _____

Interpreting Your Scores

Per discipline:

16-20: Excellent. You have competitive advantage here.
11-15: Good. Maintain and look for incremental improvements.
6-10: Needs work. Prioritize remediation.
0-5: Critical vulnerability. Address immediately.

Total score:

128-160: Stage Four or Five (Verified / Verified+)
96-127: Stage Three (Structured)
64-95: Stage Two (Aware)
32-63: Stage One (Ad Hoc)
0-31: Pre‑Stage One (unaware of narrative security)

Tools to Support the Scorecard (Beyond Spreadsheets)

You can use the scorecard manually in a spreadsheet. But tools help automate some assessments.

For criteria 1.1‑1.5 (cybersecurity):

Lucidchart or Miro for access mapping
1Password or Okta for access review logs

For criteria 2.1‑2.5 (data architecture):

Neo4j (free tier for small graphs) to visualize your knowledge graph
Schema App for structured data validation
Open Graph Debugger (Facebook) and Card Validator (Twitter) for social markup

For criteria 3.1‑3.5 (prompt engineering):

OpenAI Playground or Claude Console for prompt testing
PromptHub or Langfuse for prompt library management

For criteria 4.1‑4.5 (content strategy):

Clearscope or MarketMuse for semantic density analysis (beyond keywords)
Frase.io for content optimization with entity extraction

For criteria 5.1‑5.5 (brand architecture):

Brand24 or Mention for cross‑platform consistency monitoring
Airtable to track brand touchpoint inventory

For criteria 6.1‑6.5 (crisis management):

Jira or Asana for incident tracking and playbook management
PagerDuty or Opsgenie for incident alerting (if you automate)

For criteria 7.1‑7.5 (capital readiness):

DocSend (with tracking) for investor document distribution
Visible.vc or Carta for investor updates with trust metrics

For criteria 8.1‑8.5 (community building):

Slack or Discord with bots that flag inconsistent entity usage
Loom for scalable training on narrative consistency

Running Your Scorecard Session

Who should be in the room:

CMO or head of marketing
CISO or security lead (even if fractional)
Head of data or analytics
Head of people or culture (for Discipline Eight)
A facilitator (could be you)

Agenda (2 hours):

0:00‑0:15: Explain the scorecard and scoring rules
0:15‑1:15: Score each discipline (5‑7 minutes per discipline). Discuss each criterion. Aim for consensus.
1:15‑1:30: Calculate totals. Identify top three strengths and bottom three weaknesses.
1:30‑1:45: Prioritize. Which low‑scoring disciplines have the highest business impact?
1:45‑2:00: Assign owners and next steps for the top three remediation items.

Ground rules:

No defensiveness. Low scores are not failures. They are opportunities.
Be specific. “We sometimes do this” is a 1. “We do this consistently every week” is a 4.
Record the scores. You will run this again in six months.

Case Study: Scorecard‑Driven Improvement

A mid‑size enterprise ran the scorecard and scored:

Discipline 1 (Cybersecurity): 9 (needs work)
Discipline 2 (Data): 6 (needs work)
Discipline 3 (Prompt Engineering): 4 (critical)
Discipline 4 (Content): 11 (good)
Discipline 5 (Brand): 12 (good)
Discipline 6 (Crisis): 3 (critical)
Discipline 7 (Capital): 8 (needs work)
Discipline 8 (Community): 10 (needs work)

Total: 63 (Stage One / Ad Hoc)

They prioritized three disciplines for the next quarter:

Discipline 3 (Prompt Engineering) – because they had no retrieval auditing at all
Discipline 6 (Crisis) – because they had experienced two narrative incidents with no response plan
Discipline 2 (Data) – because entity inconsistency was driving poor retrieval

After six months of focused work, they re‑scored: